diff --git a/docs/swapi/figure_src/build_validation_table.py b/docs/swapi/figure_src/build_validation_table.py
new file mode 100644
index 000000000..5f7597045
--- /dev/null
+++ b/docs/swapi/figure_src/build_validation_table.py
@@ -0,0 +1,170 @@
+#!/usr/bin/env python3
+"""Generate the integrator-validation table in `docs/swapi/solar-wind-moments.md`."""
+
+import sys
+import types
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
+
+import numpy as np
+import pandas as pd
+
+from imap_l3_processing.constants import (
+    EV_TO_KELVIN,
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    calculate_integral,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
+
+from figure_utils import (
+    REPO_ROOT,
+    bulk_velocity_rtn_from_swapi_angles,
+    load_swapi_response,
+    peak_esa_voltage_for_proton_bulk_speed,
+    run_parallel_map,
+)
+
+_worker_state: types.SimpleNamespace | None = None
+
+_REFERENCE_INTEGRALS_PATH = (
+    REPO_ROOT / "tests" / "test_data" / "swapi" / "reference_integrals.csv"
+)
+_DOC_PATH = REPO_ROOT / "docs" / "swapi" / "solar-wind-moments.md"
+_TABLE_BEGIN = "<!-- BEGIN: validation_table"
+_TABLE_END = "<!-- END: validation_table -->"
+
+_BANDS = [
+    (r"$\lt 0.1$", 0.0, 0.1),
+    ("$0.1$ – $1$", 0.1, 1.0),
+    ("$1$ – $10$", 1.0, 10.0),
+    ("$10$ – $10^2$", 10.0, 100.0),
+    ("$10^2$ – $10^3$", 100.0, 1000.0),
+    ("$10^3$ – $10^4$", 1000.0, 1e4),
+    ("$10^4$ – $10^5$", 1e4, 1e5),
+    ("$\\geq 10^5$", 1e5, np.inf),
+]
+
+
+def _build_table(rel: np.ndarray, refs_v: np.ndarray) -> str:
+    lines = [
+        "| Reference (Hz)  |     N |  Median |     95% |     99% |     Max |",
+        "|-----------------|-------|---------|---------|---------|---------|",
+    ]
+    for label, lo, hi in _BANDS:
+        mask = (refs_v >= lo) & (refs_v < hi)
+        n = int(mask.sum())
+        if n == 0:
+            lines.append(
+                f"| {label:<15s} | {n:>5d} |       — |       — |       — |       — |"
+            )
+            continue
+        band = rel[mask]
+        med = np.median(band) * 100
+        p95 = np.percentile(band, 95) * 100
+        p99 = np.percentile(band, 99) * 100
+        mx = band.max() * 100
+        lines.append(
+            f"| {label:<15s} | {n:>5d} | {med:>6.2f}% | {p95:>6.2f}% | {p99:>6.2f}% | {mx:>6.2f}% |"
+        )
+    return "\n".join(lines)
+
+
+def _update_doc(table_md: str) -> None:
+    text = _DOC_PATH.read_text()
+    begin_idx = text.find(_TABLE_BEGIN)
+    end_idx = text.find(_TABLE_END)
+    if begin_idx < 0 or end_idx < 0 or end_idx <= begin_idx:
+        raise RuntimeError(
+            f"Could not find '{_TABLE_BEGIN}' / '{_TABLE_END}' markers in {_DOC_PATH}"
+        )
+    begin_line_end = text.find("\n", begin_idx) + 1
+    new_text = text[:begin_line_end] + table_md + "\n" + text[end_idx:]
+    _DOC_PATH.write_text(new_text)
+
+
+def _initialize_worker_state(
+    rows: list[tuple[float, float, float, float, float]],
+    peak_voltages: np.ndarray,
+    swapi_response,
+) -> None:
+    global _worker_state
+    _worker_state = types.SimpleNamespace(
+        rows=rows,
+        peak_voltages=peak_voltages,
+        swapi_response=swapi_response,
+        rotation_matrix=np.eye(3),
+    )
+
+
+def _process_one(i: int) -> float:
+    state = _worker_state
+    bulk_speed, azimuth, elevation, density, temperature_ev = state.rows[i]
+    sw = SolarWindParams(
+        density=density,
+        bulk_velocity_rtn=bulk_velocity_rtn_from_swapi_angles(
+            bulk_speed, azimuth, elevation
+        ),
+        temperature=temperature_ev * EV_TO_KELVIN,
+        mass=PROTON_MASS_KG,
+    )
+    response_grid = state.swapi_response.get_response_grid(
+        state.peak_voltages[i], PROTON_MASS_PER_CHARGE_M_P_PER_E
+    )
+    return calculate_integral(sw, response_grid, state.rotation_matrix)[0]
+
+
+def main():
+    print("Loading calibration data...")
+    swapi_response = load_swapi_response()
+    df = pd.read_csv(_REFERENCE_INTEGRALS_PATH)
+    references = df["integral"].to_numpy()
+    rows = [
+        (
+            float(row.bulk_speed),
+            float(row.bulk_azimuth),
+            float(row.bulk_elevation),
+            float(row.density),
+            float(row.temperature_ev),
+        )
+        for row in df.itertuples(index=False)
+    ]
+
+    print(f"Warming passband cache for {len(df)} rows...")
+    peak_voltages = np.array(
+        [peak_esa_voltage_for_proton_bulk_speed(r[0]) for r in rows]
+    )
+    swapi_response.warm_cache(peak_voltages)
+    for unique_voltage in np.unique(peak_voltages):
+        swapi_response.get_response_grid(
+            float(unique_voltage), PROTON_MASS_PER_CHARGE_M_P_PER_E
+        )
+
+    _initialize_worker_state(rows, peak_voltages, swapi_response)
+    optimized = np.array(
+        run_parallel_map(_process_one, len(df), desc="integrals", chunksize=64)
+    )
+
+    valid = references > 0
+    refs_v = references[valid]
+    rel = np.abs(optimized[valid] / refs_v - 1.0)
+    # Cases where both integrators round to ~0 give meaningless ratios; clamp.
+    rel = np.minimum(rel, 1.0)
+
+    print(f"\nMax |ratio - 1|:    {rel.max():.2%}")
+    print(f"Median |ratio - 1|: {np.median(rel):.2%}")
+    print(f"95th pct:           {np.percentile(rel, 95):.2%}")
+    print(f"99th pct:           {np.percentile(rel, 99):.2%}")
+
+    table_md = _build_table(rel, refs_v)
+    print()
+    print(table_md)
+    _update_doc(table_md)
+    print(f"\nUpdated {_DOC_PATH.relative_to(REPO_ROOT)}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/swapi/figure_src/figure_utils.py b/docs/swapi/figure_src/figure_utils.py
new file mode 100644
index 000000000..35202e7eb
--- /dev/null
+++ b/docs/swapi/figure_src/figure_utils.py
@@ -0,0 +1,163 @@
+"""Shared helpers for the SWAPI documentation figures."""
+
+import multiprocessing
+import os
+import sys
+import time
+from pathlib import Path
+from typing import Callable, TypeVar
+
+import numpy as np
+
+REPO_ROOT = Path(__file__).resolve().parents[3]
+sys.path.insert(0, str(REPO_ROOT))
+
+from imap_l3_processing.constants import (
+    METERS_PER_KILOMETER,
+    PROTON_CHARGE_COULOMBS,
+    PROTON_MASS_KG,
+)
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
+
+FIGURES_DIR = REPO_ROOT / "docs" / "swapi" / "figures"
+
+_INSTRUMENT_DATA_DIR = REPO_ROOT / "instrument_team_data" / "swapi"
+
+
+def load_swapi_response() -> SwapiResponse:
+    return SwapiResponse.from_files(
+        _INSTRUMENT_DATA_DIR / "imap_swapi_azimuthal-transmission_20260425_v001.csv",
+        _INSTRUMENT_DATA_DIR / "imap_swapi_central-effective-area_20260425_v001.csv",
+        _INSTRUMENT_DATA_DIR / "imap_swapi_passband-fit-coefficients_20260425_v001.csv",
+    )
+
+
+SWEEP_DURATION_S = 12.0
+BINS_PER_SWEEP = 72
+BIN_PERIOD_S = SWEEP_DURATION_S / BINS_PER_SWEEP
+SPIN_PERIOD_S = 15.13
+
+COARSE_BIN_INDICES_IN_SWEEP = np.arange(1, 63)
+
+COARSE_SWEEP_VOLTAGES_MEAN_V = np.array(
+    [
+        9895.52, 9088.69, 8348.80, 7667.55, 7042.16, 6469.31, 5941.77, 5457.31,
+        5013.22, 4603.65, 4230.77, 3886.92, 3569.16, 3278.72, 3011.13, 2766.25,
+        2539.54, 2333.83, 2144.24, 1969.31, 1808.74, 1660.86, 1525.75, 1401.82,
+        1287.58, 1182.24, 1085.15, 995.55, 914.31, 839.94, 771.70, 709.46,
+        651.59, 598.47, 549.91, 505.12, 463.89, 425.92, 391.18, 359.35,
+        329.94, 303.02, 278.25, 255.55, 234.77, 215.61, 197.95, 181.82,
+        167.04, 153.46, 140.91, 129.50, 118.91, 109.20, 100.30, 92.11,
+        84.61, 77.73, 71.40, 65.59, 60.23, 55.34,
+    ]
+)
+
+# SWAPI→RTN at the first-sweep midpoint of a real SPICE attitude near 2026-01-01;
+# spin axis (+Y column) sits ~4° off −R̂_RTN. Per-bin matrices are built by
+# spinning this about its own +Y at the nominal IMAP spin period.
+ANCHOR_ROTATION_SWAPI_TO_RTN = np.array(
+    [
+        [+0.0705, +0.9157, +0.3955],
+        [-0.9968, +0.0792, -0.0057],
+        [-0.0365, -0.3939, +0.9184],
+    ]
+).T
+ANCHOR_TIME_S = 0.5 * SWEEP_DURATION_S
+# Sign reproduces independent SPICE-derived midpoints across a 5-sweep cycle.
+SPIN_OMEGA_RAD_S = -2.0 * np.pi / SPIN_PERIOD_S
+
+
+def compute_per_bin_rotation_matrices(
+    n_sweeps: int,
+    bin_indices_in_sweep: np.ndarray = COARSE_BIN_INDICES_IN_SWEEP,
+) -> np.ndarray:
+    """Synthetic per-bin SWAPI→RTN matrices: anchor spun about its spin axis.
+
+    Returns shape (n_sweeps · n_bins, 3, 3) in sweep-major, bin-minor order.
+    """
+    sweep_index = np.repeat(np.arange(n_sweeps), len(bin_indices_in_sweep))
+    bin_index = np.tile(bin_indices_in_sweep, n_sweeps)
+    sample_times_s = sweep_index * SWEEP_DURATION_S + bin_index * BIN_PERIOD_S
+
+    spin_axis = ANCHOR_ROTATION_SWAPI_TO_RTN[:, 1] / np.linalg.norm(
+        ANCHOR_ROTATION_SWAPI_TO_RTN[:, 1]
+    )
+    delta_phi = SPIN_OMEGA_RAD_S * (sample_times_s - ANCHOR_TIME_S)
+
+    ax, ay, az = spin_axis
+    K = np.array([[0, -az, ay], [az, 0, -ax], [-ay, ax, 0]])
+    sin_dp = np.sin(delta_phi)[:, None, None]
+    one_minus_cos = (1.0 - np.cos(delta_phi))[:, None, None]
+    rot = np.eye(3) + sin_dp * K + one_minus_cos * (K @ K)
+    return rot @ ANCHOR_ROTATION_SWAPI_TO_RTN
+
+
+def peak_esa_voltage_for_proton_bulk_speed(bulk_speed_km_s: float) -> float:
+    return float(
+        PROTON_MASS_KG
+        * (bulk_speed_km_s * METERS_PER_KILOMETER) ** 2
+        / (2 * SWAPI_K_FACTOR * PROTON_CHARGE_COULOMBS)
+    )
+
+
+_T = TypeVar("_T")
+
+
+def run_parallel_map(
+    process_one: Callable[[int], _T],
+    n_items: int,
+    *,
+    desc: str,
+    chunksize: int = 10,
+) -> list[_T]:
+    """Run `process_one(i)` for i in [0, n_items) across forked workers.
+
+    Module-level `_worker_state` is inherited by children via fork — callers
+    should populate it in the parent before invoking this helper.
+    """
+    if multiprocessing.get_start_method(allow_none=True) != "fork":
+        multiprocessing.set_start_method("fork", force=True)
+    n_workers = os.cpu_count() or 1
+    print(f"Running {n_items} {desc} across {n_workers} processes...")
+    start = time.perf_counter()
+    results: list[_T | None] = [None] * n_items
+    report_every = max(1, n_items // 20)
+    with multiprocessing.get_context("fork").Pool(processes=n_workers) as pool:
+        completed = 0
+        for index, value in pool.imap_unordered(
+            _IndexedCall(process_one), range(n_items), chunksize=chunksize
+        ):
+            results[index] = value
+            completed += 1
+            if completed % report_every == 0 or completed == n_items:
+                elapsed = time.perf_counter() - start
+                print(f"  {desc}: {completed}/{n_items} ({elapsed:.1f}s)")
+    print(f"  {desc} done in {time.perf_counter() - start:.1f}s.")
+    return results  # type: ignore[return-value]
+
+
+class _IndexedCall:
+    """Picklable wrapper that returns (index, func(index)) for ordered reassembly."""
+
+    def __init__(self, func: Callable[[int], _T]):
+        self._func = func
+
+    def __call__(self, index: int):
+        return index, self._func(index)
+
+
+def bulk_velocity_rtn_from_swapi_angles(
+    bulk_speed_km_s: float, azimuth_deg: float, elevation_deg: float
+) -> np.ndarray:
+    """Inverse of `bulk_angles_in_instrument_frame` for R = identity (RTN ≡ SWAPI)."""
+    az = np.radians(azimuth_deg)
+    el = np.radians(elevation_deg)
+    horizontal = bulk_speed_km_s * np.cos(el)
+    return np.array(
+        [
+            -horizontal * np.sin(az),
+            -horizontal * np.cos(az),
+            -bulk_speed_km_s * np.sin(el),
+        ]
+    )
diff --git a/docs/swapi/figure_src/plot_alpha_peak_finding.py b/docs/swapi/figure_src/plot_alpha_peak_finding.py
new file mode 100644
index 000000000..1a8863237
--- /dev/null
+++ b/docs/swapi/figure_src/plot_alpha_peak_finding.py
@@ -0,0 +1,350 @@
+#!/usr/bin/env python3
+"""
+Plot alpha peak-finding walkthrough on real L2 SWAPI spectra.
+
+Three-panel figure (one per real-data fixture: strong alpha, hot plasma,
+cold plasma) illustrating the `get_alpha_peak_indices` peak-finder and the
+subsequent Gaussian fit on the count-rate residual:
+
+  Panel top: 5-sweep-averaged observed count rate vs ESA voltage, overlaid
+      with the frozen proton model and the detected alpha peak region shaded.
+  Panel bottom: residual (observed − proton model) at the peak bins, with
+      the fitted Gaussian.
+
+Spectra are extracted from imap_swapi_l2_sci_20260101_v005.cdf; Stage 1
+proton fits and rotation matrices are pre-computed in the test fixture at
+tests/test_data/swapi/alpha_fit_test_spectra.npz.
+
+Output: docs/swapi/figures/alpha_peak_finding.svg
+Usage:  python docs/swapi/figure_src/plot_alpha_peak_finding.py
+"""
+
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.optimize
+
+from imap_l3_processing.constants import (
+    ALPHA_MASS_PER_CHARGE_M_P_PER_E,
+    ALPHA_PARTICLE_MASS_KG,
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.alpha.calculate_alpha_solar_wind_moments import (
+    fit_solar_wind_alpha_moments,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    build_solar_wind_fit_context,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    model_solar_wind_ideal_coincidence_rates,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_model import (
+    fit_solar_wind_proton_model,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
+from imap_l3_processing.swapi.response.deadtime import deadtime_factor
+from imap_l3_processing.swapi.l3a.science.solar_wind.alpha.utils import (
+    esa_voltage_to_alpha_speed,
+    get_alpha_peak_indices,
+)
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+from figure_utils import (
+    COARSE_BIN_INDICES_IN_SWEEP,
+    FIGURES_DIR,
+    REPO_ROOT,
+    compute_per_bin_rotation_matrices,
+    load_swapi_response,
+)
+
+_FIXTURE_PATH = (
+    REPO_ROOT / "tests" / "test_data" / "swapi" / "alpha_fit_test_spectra.npz"
+)
+
+_N_SWEEPS = 5
+_N_BINS = 62
+
+_CASES = [
+    ("strong_alpha", "Strong alpha (chunk 384)"),
+    ("hot_plasma", "Hot plasma (chunk 250)"),
+    ("cold_plasma", "Cold plasma (chunk 550)"),
+]
+
+
+def _load_fixture(data, name):
+    prefix = f"{name}__"
+    return {k[len(prefix) :]: data[k] for k in data.files if k.startswith(prefix)}
+
+
+def _plot_case(axes_top, axes_bot, swapi_response, fixture, title):
+    count_rates = fixture["count_rates"]
+    voltage_per_sweep = fixture["voltage_per_sweep"]
+    esa_flat = fixture["esa_flat"]
+    proton_eff_scale = float(fixture["proton_eff_scale"])
+    alpha_eff_scale = float(fixture["alpha_eff_scale"])
+    magnetic_field_direction = fixture["b_hat_rtn"]
+    cr_flat = fixture["cr_flat"]
+
+    swapi_response.warm_cache(esa_flat)
+
+    # Rebuild geometry to match the current forward model — the stored fixture
+    # rotation matrices use an old convention.
+    rotation_matrices = compute_per_bin_rotation_matrices(
+        _N_SWEEPS, COARSE_BIN_INDICES_IN_SWEEP
+    )
+
+    proton_ctx = build_solar_wind_fit_context(
+        count_rate=cr_flat,
+        esa_voltage=esa_flat,
+        swapi_response=swapi_response,
+        central_effective_area_scale=proton_eff_scale,
+        rotation_matrices=rotation_matrices,
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    )
+    proton_moments_obj = fit_solar_wind_proton_model(proton_ctx)
+    proton_velocity_rtn = proton_moments_obj.bulk_velocity_rtn_nominal()
+    proton_sw = SolarWindParams(
+        density=proton_moments_obj.density.nominal_value,
+        bulk_velocity_rtn=proton_velocity_rtn,
+        temperature=proton_moments_obj.temperature.nominal_value,
+        mass=PROTON_MASS_KG,
+    )
+    proton_true, _ = model_solar_wind_ideal_coincidence_rates(proton_sw, proton_ctx)
+    proton_true_per_sweep = proton_true.reshape(_N_SWEEPS, _N_BINS)
+    proton_obs_per_sweep = proton_true_per_sweep * deadtime_factor(
+        proton_true_per_sweep
+    )
+    proton_bg_avg = proton_obs_per_sweep.mean(axis=0)
+    count_avg = count_rates.mean(axis=0)
+
+    alpha_moments = fit_solar_wind_alpha_moments(
+        count_rate=cr_flat,
+        esa_voltage=esa_flat,
+        measurement_time=np.zeros(len(esa_flat)),
+        swapi_response=swapi_response,
+        proton_moments=proton_moments_obj,
+        magnetic_field_direction=magnetic_field_direction,
+        alpha_effective_area_scale=alpha_eff_scale,
+        proton_effective_area_scale=proton_eff_scale,
+        rotation_matrices=rotation_matrices,
+    )
+    combined_fit_avg = None
+    alpha_contribution_avg = None
+    if np.isfinite(float(alpha_moments.density.nominal_value)):
+        alpha_ctx = build_solar_wind_fit_context(
+            count_rate=np.zeros_like(esa_flat),
+            esa_voltage=esa_flat,
+            swapi_response=swapi_response,
+            central_effective_area_scale=alpha_eff_scale,
+            rotation_matrices=rotation_matrices,
+            mass_kg=ALPHA_PARTICLE_MASS_KG,
+            mass_per_charge_m_p_per_e=ALPHA_MASS_PER_CHARGE_M_P_PER_E,
+        )
+        alpha_sw = SolarWindParams(
+            density=alpha_moments.density.nominal_value,
+            bulk_velocity_rtn=np.array(
+                [c.nominal_value for c in alpha_moments.bulk_velocity_rtn]
+            ),
+            temperature=alpha_moments.temperature.nominal_value,
+            mass=ALPHA_PARTICLE_MASS_KG,
+        )
+        alpha_true_fit, _ = model_solar_wind_ideal_coincidence_rates(
+            alpha_sw, alpha_ctx
+        )
+        combined_true = proton_true + alpha_true_fit
+        combined_fit = combined_true * deadtime_factor(combined_true)
+        combined_fit_avg = combined_fit.reshape(_N_SWEEPS, _N_BINS).mean(axis=0)
+        alpha_contribution_avg = np.maximum(combined_fit_avg - proton_bg_avg, 0.0)
+
+    energies = SWAPI_K_FACTOR * np.abs(voltage_per_sweep)
+    peak = get_alpha_peak_indices(
+        count_avg - proton_bg_avg, energies, count_avg.argmax()
+    )
+    peak_idx = np.arange(peak.start, peak.stop)
+    residual_peak = np.maximum(count_avg[peak_idx] - proton_bg_avg[peak_idx], 0.0)
+    speed_peak = esa_voltage_to_alpha_speed(voltage_per_sweep[peak_idx])
+    has_peak = peak_idx.size > 0 and residual_peak.max(initial=0.0) > 0.0
+
+    if has_peak:
+        amplitude_guess = residual_peak.max()
+        mean_guess = float(speed_peak[int(np.argmax(residual_peak))])
+        try:
+            (A_fit, mu_fit, sigma_fit), _ = scipy.optimize.curve_fit(
+                lambda v, A, mu, sigma: A
+                * np.exp(-((v - mu) ** 2) / (2 * sigma**2)),
+                speed_peak,
+                residual_peak,
+                p0=[amplitude_guess, mean_guess, 50.0],
+                bounds=([0, 0, 0], [np.inf, np.inf, np.inf]),
+            )
+        except RuntimeError:
+            A_fit, mu_fit, sigma_fit = amplitude_guess, mean_guess, 50.0
+    else:
+        A_fit, mu_fit, sigma_fit = 0.0, 0.0, 1.0
+
+    abs_voltage = np.abs(voltage_per_sweep)
+    sort_idx = np.argsort(abs_voltage)
+    abs_voltage_sorted = abs_voltage[sort_idx]
+
+    axes_top.plot(
+        abs_voltage_sorted,
+        count_avg[sort_idx],
+        ".",
+        color="tab:blue",
+        markersize=4,
+        label="Observed (5-sweep avg)",
+        zorder=3,
+    )
+    axes_top.plot(
+        abs_voltage_sorted,
+        proton_bg_avg[sort_idx],
+        color="tab:orange",
+        lw=1.5,
+        label="Proton model",
+        zorder=2,
+    )
+    if combined_fit_avg is not None:
+        axes_top.plot(
+            abs_voltage_sorted,
+            combined_fit_avg[sort_idx],
+            color="tab:purple",
+            lw=1.5,
+            linestyle="--",
+            label="Combined fit (p + α)",
+            zorder=2,
+        )
+
+    if has_peak:
+        peak_voltages = abs_voltage[peak_idx]
+        v_lo, v_hi = peak_voltages.min(), peak_voltages.max()
+        axes_top.axvspan(
+            v_lo, v_hi, alpha=0.15, color="tab:green", label="Alpha peak", zorder=1
+        )
+
+    axes_top.plot(
+        abs_voltage[peak_idx],
+        count_avg[peak_idx],
+        "o",
+        color="tab:green",
+        markersize=5,
+        markerfacecolor="none",
+        lw=1.2,
+        zorder=4,
+    )
+
+    axes_top.set_xscale("log")
+    axes_top.set_yscale("log")
+    axes_top.set_ylim(bottom=0.5)
+    axes_top.set_ylabel("Count rate [Hz]")
+    axes_top.set_title(title, fontsize=10)
+    axes_top.legend(fontsize=7, loc="upper left")
+    axes_top.grid(True, which="both", alpha=0.2)
+
+    all_speeds = esa_voltage_to_alpha_speed(voltage_per_sweep)
+    all_residual = np.maximum(count_avg - proton_bg_avg, 0.0)
+
+    axes_bot.vlines(
+        all_speeds[sort_idx],
+        0,
+        all_residual[sort_idx],
+        colors="lightgrey",
+        linewidth=1.5,
+        zorder=1,
+    )
+    axes_bot.plot(
+        all_speeds[sort_idx],
+        all_residual[sort_idx],
+        ".",
+        color="grey",
+        markersize=3,
+        zorder=1,
+        label="Residual (all bins)",
+    )
+
+    axes_bot.vlines(
+        speed_peak, 0, residual_peak, colors="tab:green", linewidth=2, zorder=2
+    )
+    axes_bot.plot(
+        speed_peak,
+        residual_peak,
+        "o",
+        color="tab:green",
+        markersize=5,
+        zorder=2,
+        label="Peak bins",
+    )
+
+    if has_peak:
+        v_fine = np.linspace(speed_peak.min() - 50, speed_peak.max() + 50, 200)
+        gauss_fine = A_fit * np.exp(-((v_fine - mu_fit) ** 2) / (2 * sigma_fit**2))
+        axes_bot.plot(
+            v_fine,
+            gauss_fine,
+            color="tab:red",
+            lw=1.5,
+            linestyle=":",
+            label=rf"Gaussian (init. guess): $v_\alpha$={mu_fit:.0f} km/s",
+            zorder=3,
+        )
+
+    if alpha_contribution_avg is not None:
+        alpha_speed = np.linalg.norm(
+            [c.nominal_value for c in alpha_moments.bulk_velocity_rtn]
+        )
+        axes_bot.plot(
+            all_speeds[sort_idx],
+            alpha_contribution_avg[sort_idx],
+            color="tab:purple",
+            lw=1.5,
+            linestyle="--",
+            label=rf"Moments fit: $v_\alpha$={alpha_speed:.0f} km/s",
+            zorder=4,
+        )
+
+    axes_bot.set_xlabel(r"$\alpha$ speed [km/s]")
+    axes_bot.set_ylabel("Residual [Hz]")
+    axes_bot.legend(fontsize=7, loc="upper right")
+    axes_bot.grid(True, alpha=0.2)
+    y_top = max(residual_peak.max(initial=0.0), A_fit) * 1.3
+    axes_bot.set_ylim(0, max(y_top, 10))
+
+
+def main():
+    print("Loading calibration data...")
+    swapi_response = load_swapi_response()
+
+    data = np.load(_FIXTURE_PATH)
+    n_cases = len(_CASES)
+    fig, axes = plt.subplots(
+        2,
+        n_cases,
+        figsize=(4.5 * n_cases, 7),
+        gridspec_kw={"height_ratios": [2, 1.2]},
+    )
+    fig.suptitle(
+        r"Alpha peak-finding on real L2 spectra (imap\_swapi\_l2\_sci\_20260101)",
+        fontsize=11,
+    )
+
+    for col, (case_name, case_title) in enumerate(_CASES):
+        print(f"Plotting {case_name}...")
+        fixture = _load_fixture(data, case_name)
+        _plot_case(axes[0, col], axes[1, col], swapi_response, fixture, case_title)
+
+    fig.tight_layout()
+    FIGURES_DIR.mkdir(parents=True, exist_ok=True)
+    out = FIGURES_DIR / "alpha_peak_finding.svg"
+    fig.savefig(out, bbox_inches="tight")
+    print(f"Saved {out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/swapi/figure_src/plot_calibration_curves.py b/docs/swapi/figure_src/plot_calibration_curves.py
new file mode 100644
index 000000000..ba0628877
--- /dev/null
+++ b/docs/swapi/figure_src/plot_calibration_curves.py
@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+"""
+Plot the SWAPI central effective area and azimuthal transmission calibration curves.
+
+Output: docs/swapi/figures/calibration_curves.svg
+Usage:  python docs/swapi/figure_src/plot_calibration_curves.py
+"""
+
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
+from figure_utils import FIGURES_DIR, load_swapi_response
+
+_AREA_CSV = (
+    Path(__file__).resolve().parents[3]
+    / "instrument_team_data"
+    / "swapi"
+    / "imap_swapi_central-effective-area_20260425_v001.csv"
+)
+
+
+def main():
+    swapi_response = load_swapi_response()
+
+    area_df = pd.read_csv(_AREA_CSV)
+    voltages = area_df["esa_voltage"].to_numpy()
+    eff_area = area_df["effective_area"].to_numpy()
+
+    transmission = swapi_response._azimuthal_transmission
+    azimuths = (
+        np.arange(len(transmission)) * SwapiResponse.AZIMUTHAL_TRANSMISSION_SPACING_DEG
+    )
+
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
+
+    ax1.plot(voltages, eff_area, color="steelblue", linewidth=1.5)
+    ax1.set_xscale("log")
+    ax1.set_xlabel("ESA Voltage ($|V|$) [V]")
+    ax1.set_ylabel("Central Effective Area $\\mathcal{A}_0(V)$ (cm²)")
+    ax1.grid(True, which="both", alpha=0.3)
+
+    full_az = np.concatenate([-azimuths[::-1], azimuths])
+    full_tx = np.concatenate([transmission[::-1], transmission])
+    ax2.semilogy(
+        full_az,
+        full_tx,
+        color="darkorange",
+        linewidth=1.5,
+    )
+    ax2.set_xlabel(r"Azimuth Angle ($\phi$) [$^\circ$]")
+    ax2.set_ylabel("Azimuthal Transmission $T(\\phi)$")
+    ax2.axvspan(-20, 20, alpha=0.12, color="steelblue", label="Sunglasses (SG)")
+    ax2.axvspan(20, 150, alpha=0.12, color="darkorange", label="Open aperture (OA)")
+    ax2.axvspan(-150, -20, alpha=0.12, color="darkorange")
+    ax2.set_xlim(-180, 180)
+    ax2.set_ylim(1e-5, 1.5e0)
+    ax2.set_xticks([-150, -90, -20, 0, 20, 90, 150])
+    ax2.legend(fontsize=8)
+    ax2.grid(True, which="both", alpha=0.3)
+
+    fig.tight_layout()
+    FIGURES_DIR.mkdir(parents=True, exist_ok=True)
+    out = FIGURES_DIR / "calibration_curves.svg"
+    fig.savefig(out, bbox_inches="tight")
+    print(f"Saved {out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/swapi/figure_src/plot_fit_accuracy.py b/docs/swapi/figure_src/plot_fit_accuracy.py
new file mode 100644
index 000000000..432cf95da
--- /dev/null
+++ b/docs/swapi/figure_src/plot_fit_accuracy.py
@@ -0,0 +1,406 @@
+#!/usr/bin/env python3
+"""
+Scatter plots comparing the SWAPI proton-moments fit against ground truth on
+**real solar wind conditions** sampled from WIND/SWE 2-min ASCII data.
+
+Uses the 62 coarse-sweep bins (indices 1..62 of a 72-bin sweep) to show that
+the fit recovers (n, T, v_R, v_T, v_N) without fine-sweep coverage.
+
+Per-bin SWAPI→RTN rotation matrices are generated synthetically by spinning a
+single anchor matrix (a real SPICE attitude near 2026-01-01) about its own
+spin axis at the nominal SWAPI spin period.
+
+Synthetic count rates are produced from the SWAPI forward model (5 sweeps per
+fit, Poisson noise); the ground truth is read from a CSV produced by
+scripts/swapi/sample_wind_solar_wind.py.
+
+Generate the CSV first:
+  conda run -n imapL3 python scripts/swapi/sample_wind_solar_wind.py \
+      --year 2025 --n 10000 --seed 7
+
+Output: docs/swapi/figures/fit_accuracy.svg
+Usage:  conda run -n imapL3 python docs/swapi/figure_src/plot_fit_accuracy.py
+"""
+
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
+
+import types
+
+import numpy as np
+import pandas as pd
+from uncertainties import UFloat
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+from imap_l3_processing.constants import (
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
+)
+from imap_l3_processing.swapi.constants import SWAPI_LIVETIME_S
+from figure_utils import (
+    COARSE_BIN_INDICES_IN_SWEEP,
+    COARSE_SWEEP_VOLTAGES_MEAN_V,
+    REPO_ROOT,
+    compute_per_bin_rotation_matrices,
+    load_swapi_response,
+    run_parallel_map,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_model import (
+    fit_solar_wind_proton_model,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.initial_guess import (
+    calculate_initial_guess,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    model_solar_wind_ideal_coincidence_rates,
+)
+from imap_l3_processing.swapi.response.deadtime import deadtime_factor
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    build_solar_wind_fit_context,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
+
+_N_SWEEPS = 5
+_N_BINS = len(COARSE_SWEEP_VOLTAGES_MEAN_V)
+
+_worker_state: types.SimpleNamespace | None = None
+
+
+def main():
+    csv_path = REPO_ROOT / "docs/swapi/figure_src/wind_solar_wind_samples_2025.csv"
+    ground_truth_params = _load_wind_samples(csv_path)
+    _initialize_worker_state(ground_truth_params)
+    data = _run_fits(n_samples=len(ground_truth_params[0]))
+    _plot_results(data)
+
+
+def _load_wind_samples(csv_path: Path) -> tuple[np.ndarray, ...]:
+    """Load WIND-derived ground-truth proton parameters from CSV."""
+    if not csv_path.exists():
+        raise FileNotFoundError(
+            f"Missing WIND samples CSV: {csv_path}. "
+            f"Run scripts/swapi/sample_wind_solar_wind.py first."
+        )
+    cols = np.genfromtxt(
+        csv_path, delimiter=",", names=True, dtype=None, encoding="utf-8"
+    )
+    return (
+        cols["v_R_km_s"].astype(float),
+        cols["proton_temperature_K"].astype(float),
+        cols["proton_density_cm3"].astype(float),
+        cols["v_T_km_s"].astype(float),
+        cols["v_N_km_s"].astype(float),
+    )
+
+
+def _initialize_worker_state(ground_truth_params: tuple[np.ndarray, ...]) -> None:
+    global _worker_state
+
+    print(
+        f"Using {_N_BINS} coarse-sweep bins, "
+        f"{COARSE_SWEEP_VOLTAGES_MEAN_V.min():.1f}–{COARSE_SWEEP_VOLTAGES_MEAN_V.max():.1f} V"
+    )
+
+    swapi_response = load_swapi_response()
+    all_esa_voltages = np.tile(COARSE_SWEEP_VOLTAGES_MEAN_V, _N_SWEEPS)
+    swapi_response.warm_cache(all_esa_voltages)
+    per_bin_rotation_matrices = compute_per_bin_rotation_matrices(
+        _N_SWEEPS, COARSE_BIN_INDICES_IN_SWEEP
+    )
+    base_ctx = build_solar_wind_fit_context(
+        count_rate=np.ones_like(all_esa_voltages),
+        esa_voltage=all_esa_voltages,
+        swapi_response=swapi_response,
+        central_effective_area_scale=1.0,
+        rotation_matrices=per_bin_rotation_matrices,
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    )
+    _worker_state = types.SimpleNamespace(
+        ground_truth_params=ground_truth_params,
+        swapi_response=swapi_response,
+        all_esa_voltages=all_esa_voltages,
+        per_bin_rotation_matrices=per_bin_rotation_matrices,
+        base_ctx=base_ctx,
+    )
+
+
+def _run_fits(n_samples: int) -> pd.DataFrame:
+    rows = run_parallel_map(_process_one, n_samples, desc="fits", chunksize=10)
+    data = pd.DataFrame(rows)
+    print(f"Bad-fit flags: {data['bad_flag'].sum()}/{n_samples}")
+    out_csv = Path("/tmp/fit_accuracy_results.csv")
+    data.to_csv(out_csv, index=False)
+    print(f"Saved fit results to {out_csv}")
+    return data
+
+
+def _process_one(i):
+    ws = _worker_state
+    radial_speeds, temperatures, densities, tangential_speeds, normal_speeds = (
+        ws.ground_truth_params
+    )
+    radial_speed = float(radial_speeds[i])
+    temperature = float(temperatures[i])
+    density = float(densities[i])
+    tangential_speed = float(tangential_speeds[i])
+    normal_speed = float(normal_speeds[i])
+
+    truth_params = SolarWindParams(
+        density=density,
+        bulk_velocity_rtn=np.array([radial_speed, tangential_speed, normal_speed]),
+        temperature=temperature,
+        mass=PROTON_MASS_KG,
+    )
+    count_rates, _ = model_solar_wind_ideal_coincidence_rates(truth_params, ws.base_ctx)
+    count_rates = count_rates * deadtime_factor(count_rates)
+    count_rates = (
+        np.random.default_rng(i)
+        .poisson(np.maximum(count_rates * SWAPI_LIVETIME_S, 0.0))
+        .astype(float)
+        / SWAPI_LIVETIME_S
+    )
+
+    fit_ctx = build_solar_wind_fit_context(
+        count_rate=count_rates,
+        esa_voltage=ws.all_esa_voltages,
+        swapi_response=ws.swapi_response,
+        central_effective_area_scale=1.0,
+        rotation_matrices=ws.per_bin_rotation_matrices,
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    )
+    try:
+        initial_guess = calculate_initial_guess(fit_ctx)
+    except Exception:
+        initial_guess = SolarWindParams(
+            density=float("nan"),
+            bulk_velocity_rtn=np.array([float("nan")] * 3),
+            temperature=float("nan"),
+            mass=PROTON_MASS_KG,
+        )
+    try:
+        result = fit_solar_wind_proton_model(fit_ctx)
+    except Exception as e:
+        print(f"  case {i}: fit failed ({type(e).__name__}: {e}); flagging bad")
+        from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_model import (
+            ProtonSolarWindFitResult,
+        )
+        from uncertainties import ufloat
+
+        nan_uf = ufloat(float("nan"), float("nan"))
+        result = ProtonSolarWindFitResult(
+            density=nan_uf,
+            temperature=nan_uf,
+            bulk_velocity_rtn=(nan_uf, nan_uf, nan_uf),
+            bad_fit_flag=1,
+        )
+
+    return {
+        "true_density": density,
+        "true_temperature": temperature,
+        "true_radial_speed": radial_speed,
+        "true_tangential_speed": tangential_speed,
+        "true_normal_speed": normal_speed,
+        "init_density": initial_guess.density,
+        "init_temperature": initial_guess.temperature,
+        "init_radial_speed": float(initial_guess.bulk_velocity_rtn[0]),
+        "init_tangential_speed": float(initial_guess.bulk_velocity_rtn[1]),
+        "init_normal_speed": float(initial_guess.bulk_velocity_rtn[2]),
+        "fit_density": _nominal(result.density),
+        "fit_temperature": _nominal(result.temperature),
+        "fit_radial_speed": _nominal(result.bulk_velocity_rtn[0]),
+        "fit_tangential_speed": _nominal(result.bulk_velocity_rtn[1]),
+        "fit_normal_speed": _nominal(result.bulk_velocity_rtn[2]),
+        "fit_density_sigma": _sigma(result.density),
+        "fit_temperature_sigma": _sigma(result.temperature),
+        "fit_radial_speed_sigma": _sigma(result.bulk_velocity_rtn[0]),
+        "fit_tangential_speed_sigma": _sigma(result.bulk_velocity_rtn[1]),
+        "fit_normal_speed_sigma": _sigma(result.bulk_velocity_rtn[2]),
+        "bad_flag": bool(result.bad_fit_flag),
+    }
+
+
+def _nominal(x):
+    return x.nominal_value if isinstance(x, UFloat) else x
+
+
+def _sigma(x):
+    return x.std_dev if isinstance(x, UFloat) else float("nan")
+
+
+def _plot_results(data: pd.DataFrame) -> None:
+    n_samples = len(data)
+    good = ~data["bad_flag"]
+    n_bad = data["bad_flag"].sum()
+
+    plot_columns = [
+        (
+            "Density (cm⁻³)",
+            "true_density",
+            "init_density",
+            "fit_density",
+            "fit_density_sigma",
+            "log",
+        ),
+        (
+            "Temperature (K)",
+            "true_temperature",
+            "init_temperature",
+            "fit_temperature",
+            "fit_temperature_sigma",
+            "log",
+        ),
+        (
+            "$v_R$ (km/s)",
+            "true_radial_speed",
+            "init_radial_speed",
+            "fit_radial_speed",
+            "fit_radial_speed_sigma",
+            "linear",
+        ),
+        (
+            "$v_T$ (km/s)",
+            "true_tangential_speed",
+            "init_tangential_speed",
+            "fit_tangential_speed",
+            "fit_tangential_speed_sigma",
+            "linear",
+        ),
+        (
+            "$v_N$ (km/s)",
+            "true_normal_speed",
+            "init_normal_speed",
+            "fit_normal_speed",
+            "fit_normal_speed_sigma",
+            "linear",
+        ),
+    ]
+
+    fig, axes = plt.subplots(1, 5, figsize=(17, 4))
+    fig.suptitle(
+        f"Initial guess vs. final optimizer vs. WIND ground truth\n"
+        f"({n_samples} real solar wind cases from WIND/SWE 2-min 2025, "
+        f"{_N_SWEEPS} sweeps × {_N_BINS} coarse-sweep bins, Poisson noise)",
+        fontsize=11,
+    )
+
+    color_initial_guess = "tab:orange"
+    color_final_fit = "tab:blue"
+
+    for ax, (label, true_key, init_key, fit_key, fit_sigma_key, scale) in zip(
+        axes, plot_columns
+    ):
+        truth = data[true_key]
+        init = data[init_key]
+        fit = data[fit_key]
+        fit_sigma = data[fit_sigma_key]
+
+        lo = np.nanmin(np.concatenate([truth, init, fit]))
+        hi = np.nanmax(np.concatenate([truth, init, fit]))
+        ref = np.linspace(lo, hi, 200)
+        ax.plot(ref, ref, "k--", lw=0.8, alpha=0.4, zorder=0)
+
+        ax.scatter(
+            truth[good],
+            init[good],
+            s=6,
+            alpha=0.35,
+            color=color_initial_guess,
+            marker="o",
+            zorder=2,
+            label="Initial guess",
+            rasterized=True
+        )
+        ax.errorbar(
+            truth[good],
+            fit[good],
+            yerr=fit_sigma[good],
+            fmt="^",
+            markersize=2.5,
+            color=color_final_fit,
+            ecolor=color_final_fit,
+            elinewidth=0.4,
+            capsize=0,
+            alpha=0.45,
+            zorder=3,
+            label="Final fit",
+            rasterized=True,
+        )
+
+        if n_bad:
+            ax.scatter(
+                truth[~good],
+                init[~good],
+                s=30,
+                alpha=0.9,
+                color=color_initial_guess,
+                marker="X",
+                edgecolors="k",
+                linewidths=0.4,
+                zorder=4,
+                rasterized=True
+            )
+            ax.scatter(
+                truth[~good],
+                fit[~good],
+                s=30,
+                alpha=0.9,
+                color=color_final_fit,
+                marker="X",
+                edgecolors="k",
+                linewidths=0.4,
+                zorder=4,
+                rasterized=True
+            )
+
+        ax.set_xlabel(f"True {label}", fontsize=9)
+        ax.set_ylabel("Estimated", fontsize=9)
+        ax.set_title(label, fontsize=10)
+        ax.tick_params(labelsize=8)
+        ax.grid(True, alpha=0.25)
+
+        if scale == "log":
+            ax.set_xscale("log")
+            ax.set_yscale("log")
+
+        ax.annotate(
+            f"Init  {_rmse_label(truth[good], init[good], scale)}\n"
+            f"Fit    {_rmse_label(truth[good], fit[good], scale)}",
+            xy=(0.04, 0.97),
+            xycoords="axes fraction",
+            va="top",
+            ha="left",
+            fontsize=6.5,
+            bbox=dict(boxstyle="round,pad=0.3", fc="white", alpha=0.75),
+        )
+
+    axes[0].legend(fontsize=8, loc="lower right", framealpha=0.8)
+    fig.tight_layout()
+
+    out_dir = REPO_ROOT / "docs" / "swapi" / "figures"
+    out_dir.mkdir(parents=True, exist_ok=True)
+    out_path = out_dir / "fit_accuracy.svg"
+    fig.savefig(out_path, bbox_inches="tight", dpi=200)
+    print(f"Saved {out_path}")
+
+
+def _rmse_label(truth, estimated, scale):
+    mask = np.isfinite(estimated)
+    if scale == "log":
+        rmse = np.sqrt(
+            np.mean((np.log10(estimated[mask]) - np.log10(truth[mask])) ** 2)
+        )
+        return f"RMSE log₁₀ = {rmse:.3f}"
+    else:
+        rmse = np.sqrt(np.mean((estimated[mask] - truth[mask]) ** 2))
+        return f"RMSE = {rmse:.1f} km/s"
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/swapi/figure_src/plot_passband_boundaries.py b/docs/swapi/figure_src/plot_passband_boundaries.py
new file mode 100644
index 000000000..862d543ee
--- /dev/null
+++ b/docs/swapi/figure_src/plot_passband_boundaries.py
@@ -0,0 +1,160 @@
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+from imap_l3_processing.swapi.response.passband_grid import PassbandGrid
+from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
+from figure_utils import FIGURES_DIR, load_swapi_response
+
+_ELEVATION_DISPLAY_LIMIT_DEG = 15.0
+_ACTIVE_ELEVATION_SAMPLE_COUNT = 300
+
+
+def main():
+    swapi_response = load_swapi_response()
+    esa_voltages = representative_esa_voltages(swapi_response)
+    swapi_response.warm_cache(esa_voltages)
+
+    n_columns = len(esa_voltages)
+    n_rows = 2
+    figure, axes = plt.subplots(
+        n_rows, n_columns, figsize=(5 * n_columns, 4 * n_rows), sharey=True
+    )
+
+    last_image = None
+    for column, esa_voltage in enumerate(esa_voltages):
+        response_grid = swapi_response.get_response_grid(esa_voltage, 1.0)
+        central_speed = response_grid.central_speed
+
+        for row, region in enumerate(["open_aperture", "sunglasses"]):
+            grid = (
+                response_grid.oa_passband
+                if region == "open_aperture"
+                else response_grid.sg_passband
+            )
+            axis = axes[row, column]
+            last_image = plot_region_panel(
+                axis, grid, region, esa_voltage, central_speed
+            )
+            if column == 0:
+                axis.set_ylabel("Elevation (deg)")
+
+    figure.tight_layout()
+    colorbar = figure.colorbar(last_image, ax=axes, fraction=0.03, pad=0.02)
+    colorbar.set_label("Passband value")
+
+    FIGURES_DIR.mkdir(parents=True, exist_ok=True)
+    output_path = FIGURES_DIR / "passband_boundaries.svg"
+    figure.savefig(output_path, bbox_inches="tight")
+    print(f"Saved {output_path}")
+
+
+def representative_esa_voltages(swapi_response: SwapiResponse) -> list[float]:
+    voltage_limits = swapi_response._passband_esa_voltage_limits
+    voltage_minimum = min(low for low, _ in voltage_limits.values())
+    voltage_maximum = max(high for _, high in voltage_limits.values())
+    voltage_geometric_mean = float(np.sqrt(voltage_minimum * voltage_maximum))
+    return [voltage_minimum, voltage_geometric_mean, voltage_maximum]
+
+
+def plot_region_panel(
+    axis,
+    grid: PassbandGrid,
+    region: str,
+    esa_voltage: float,
+    central_speed: float,
+):
+    label = "Open Aperture (OA)" if region == "open_aperture" else "Sunglasses (SG)"
+
+    elevations, speed_ratios = grid_axis_coordinates(grid, grid.values)
+
+    active_elevations = np.linspace(
+        grid.elevation_range[0], grid.elevation_range[1], _ACTIVE_ELEVATION_SAMPLE_COUNT
+    )
+    lower_speed_ratios = _vectorized_bracket(
+        grid, grid.min_boundary, active_elevations, np.minimum
+    )
+    upper_speed_ratios = _vectorized_bracket(
+        grid, grid.max_boundary, active_elevations, np.maximum
+    )
+
+    image = draw_transmission_heatmap(
+        axis, grid.values, elevations, speed_ratios
+    )
+    draw_integration_window_outline(
+        axis, active_elevations, lower_speed_ratios, upper_speed_ratios
+    )
+
+    axis.set_xlabel("Speed ratio (v / v_central)")
+    axis.set_title(f"{label}  |  {esa_voltage:.1f} V  ({central_speed:.0f} km/s)")
+    axis.set_ylim(-_ELEVATION_DISPLAY_LIMIT_DEG, _ELEVATION_DISPLAY_LIMIT_DEG)
+    return image
+
+
+def grid_axis_coordinates(
+    grid: PassbandGrid, transmission_values: np.ndarray
+) -> tuple[np.ndarray, np.ndarray]:
+    n_elevations, n_speed_ratios = transmission_values.shape
+    elevations = grid.min_elevation + np.arange(n_elevations) * grid.elevation_spacing
+    speed_ratios = (
+        grid.min_speed_ratio + np.arange(n_speed_ratios) * grid.speed_ratio_spacing
+    )
+    return elevations, speed_ratios
+
+
+def draw_transmission_heatmap(
+    axis,
+    transmission_values: np.ndarray,
+    elevations: np.ndarray,
+    speed_ratios: np.ndarray,
+):
+    extent = [speed_ratios[0], speed_ratios[-1], elevations[0], elevations[-1]]
+    return axis.imshow(
+        transmission_values,
+        origin="lower",
+        aspect="auto",
+        extent=extent,
+        cmap="gist_heat",
+        interpolation="nearest",
+    )
+
+
+def _vectorized_bracket(
+    grid: PassbandGrid, boundary: np.ndarray, elevations: np.ndarray, combine
+) -> np.ndarray:
+    """Per-elevation speed-ratio boundary as `combine` (np.minimum or np.maximum)
+    of the two grid rows whose elevations bracket each query. Boundaries are
+    indexed on the grid's uniform elevation axis (same as `interpolate_passband`);
+    off-grid queries clamp to row 0 / row n-1."""
+    n_rows = boundary.shape[0]
+    last = n_rows - 1
+    i_float = (elevations - grid.min_elevation) / grid.elevation_spacing
+    lower = np.clip(np.floor(i_float).astype(int), 0, last)
+    upper = np.clip(lower + 1, 0, last)
+    return combine(boundary[lower], boundary[upper])
+
+
+def draw_integration_window_outline(
+    axis,
+    active_elevations: np.ndarray,
+    lower_speed_ratios: np.ndarray,
+    upper_speed_ratios: np.ndarray,
+):
+    closed_x = np.concatenate(
+        [lower_speed_ratios, upper_speed_ratios[::-1], [lower_speed_ratios[0]]]
+    )
+    closed_y = np.concatenate(
+        [active_elevations, active_elevations[::-1], [active_elevations[0]]]
+    )
+    axis.plot(closed_x, closed_y, color="tab:blue", linewidth=2.5)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/swapi/figure_src/plot_real_data_fit.py b/docs/swapi/figure_src/plot_real_data_fit.py
new file mode 100644
index 000000000..eec174643
--- /dev/null
+++ b/docs/swapi/figure_src/plot_real_data_fit.py
@@ -0,0 +1,922 @@
+#!/usr/bin/env python3
+"""
+Real-data example: SWAPI proton-moments fit on 5 sweeps + averaged sweep.
+
+Picks a real L2 date/sweep block where the fine-sweep bins (63..71) bracket
+the proton peak, fits twice per chunk (full SCIENCE bins vs coarse-only bins),
+and overlays both forward-modelled spectra on each sweep's measured count
+rate. The 6th column shows the average of the 5 sweeps with the same overlays.
+
+The script downloads the L2 CDF and SPICE kernels itself via imap-data-access.
+The IMAP API key is read from the IMAP_API_KEY environment variable.
+WIND/SWE 2-min ground truth at the chunk epoch is fetched the same way as
+plot_fit_accuracy.py — cached under ~/.cache/imap_l3/wind_swe_2m/.
+
+Output: docs/swapi/figures/real_data_fit.svg
+Usage:  conda run -n imapL3 python docs/swapi/figure_src/plot_real_data_fit.py
+"""
+
+import json
+import os
+import sys
+import urllib.request
+from datetime import datetime, timedelta
+from pathlib import Path
+from urllib.parse import urlparse
+
+import imap_data_access
+import matplotlib
+import numpy as np
+import requests
+import spacepy.pycdf
+import spiceypy
+from matplotlib.transforms import blended_transform_factory
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
+
+from imap_l3_processing.constants import (
+    BOLTZMANN_CONSTANT_JOULES_PER_KELVIN,
+    ONE_SECOND_IN_NANOSECONDS,
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
+)
+import scipy.optimize
+
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    build_solar_wind_fit_context,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    model_solar_wind_ideal_coincidence_rates,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_model import (
+    fit_solar_wind_proton_model,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
+from imap_l3_processing.swapi.l3a.utils import (
+    get_spacecraft_velocity_rtn,
+    get_swapi_geometry,
+)
+from imap_l3_processing.swapi.response.deadtime import deadtime_factor
+from imap_l3_processing.swapi.constants import (
+    SWAPI_COARSE_SWEEP_BINS,
+    SWAPI_FINE_SWEEP_BINS,
+    SWAPI_K_FACTOR,
+    SWAPI_L2_K_FACTOR,
+    SWAPI_SCIENCE_BINS,
+)
+from imap_l3_processing.utils import SpiceKernelTypes
+from figure_utils import FIGURES_DIR, REPO_ROOT, load_swapi_response
+
+_DOC_PATH = REPO_ROOT / "docs" / "swapi" / "solar-wind-moments.md"
+_TABLE_BEGIN = "<!-- BEGIN: real_data_table"
+_TABLE_END = "<!-- END: real_data_table -->"
+
+# 2026-02-01: 5-sweep block at sweep_start=6853, identified by inspecting the
+# day's fine-sweep voltages — fine bins span 5..334 V and bracket the ~257 V
+# proton peak. 60-second chunk covers 22:50:36 .. 22:51:36 UT.
+DATE_YYYYMMDD = "20260201"
+SWEEP_START = 6853
+N_SWEEPS = 5
+
+WIND_YEAR = 2026
+WIND_CACHE_DIR = Path.home() / ".cache" / "imap_l3" / "wind_swe_2m"
+WIND_BASE_URL = "https://spdf.gsfc.nasa.gov/pub/data/wind/swe/ascii/2-min"
+
+
+def main():
+    if not os.environ.get("IMAP_API_KEY"):
+        raise SystemExit(
+            "IMAP_API_KEY environment variable is not set. "
+            "Export it before running this script."
+        )
+    cdf_path = _download_l2(DATE_YYYYMMDD)
+    _furnish_spice_around(DATE_YYYYMMDD)
+
+    sweep_start = SWEEP_START
+    epoch_ns, count_rate, esa_voltage = _read_5_sweep_block(
+        cdf_path, sweep_start, N_SWEEPS
+    )
+    chunk_center_ns = int(epoch_ns[0]) + 30 * ONE_SECOND_IN_NANOSECONDS
+    chunk_center_dt = _tt2000ns_to_datetime(chunk_center_ns)
+    print(
+        f"Chunk: {DATE_YYYYMMDD} sweeps {sweep_start}..{sweep_start + N_SWEEPS - 1} "
+        f"(center {chunk_center_dt.isoformat()} UT)"
+    )
+
+    swapi_response = load_swapi_response()
+    swapi_response.warm_cache(esa_voltage.flatten() / SWAPI_L2_K_FACTOR)
+
+    science_result = _fit_for_bins(
+        SWAPI_SCIENCE_BINS, count_rate, esa_voltage, epoch_ns, swapi_response
+    )
+    coarse_result = _fit_for_bins(
+        SWAPI_COARSE_SWEEP_BINS, count_rate, esa_voltage, epoch_ns, swapi_response
+    )
+
+    sc_velocity_rtn = get_spacecraft_velocity_rtn(chunk_center_ns)
+    science_result["bulk_velocity_rtn_sun"] = (
+        np.array(
+            [c.nominal_value for c in science_result["fit_result"].bulk_velocity_rtn]
+        )
+        + sc_velocity_rtn
+    )
+    coarse_result["bulk_velocity_rtn_sun"] = (
+        np.array(
+            [c.nominal_value for c in coarse_result["fit_result"].bulk_velocity_rtn]
+        )
+        + sc_velocity_rtn
+    )
+
+    print("\n--- Fit results ---")
+    print(f"  Science bins (1..71, includes fine):")
+    _print_fit(science_result, sc_velocity_rtn)
+    print(f"  Coarse-only bins (1..62):")
+    _print_fit(coarse_result, sc_velocity_rtn)
+
+    print("\n--- Bootstrap σ on the all-bins fit (B=300, warm-LM, xtol=1e-3) ---")
+    boot = _bootstrap_sigmas(
+        science_result,
+        B=300,
+        xtol=1e-3,
+        seed=42,
+        sc_velocity_rtn=sc_velocity_rtn,
+    )
+    print(
+        f"  kept {boot['B_kept']}/{boot['B_total']} resamples\n"
+        f"  σ_n  = {boot['n_sigma']:.4f} cm^-3   (HC3: {science_result['fit_result'].density.std_dev:.4f})\n"
+        f"  σ_T  = {boot['T_sigma']:.3e} K       (HC3: {science_result['fit_result'].temperature.std_dev:.3e})\n"
+        f"  σ_vR = {boot['vR_sigma']:.3f} km/s   (HC3: {science_result['fit_result'].bulk_velocity_rtn[0].std_dev:.3f})\n"
+        f"  σ_vT = {boot['vT_sigma']:.3f} km/s   (HC3: {science_result['fit_result'].bulk_velocity_rtn[1].std_dev:.3f})\n"
+        f"  σ_vN = {boot['vN_sigma']:.3f} km/s   (HC3: {science_result['fit_result'].bulk_velocity_rtn[2].std_dev:.3f})"
+    )
+
+    wind = _wind_value_at(chunk_center_dt)
+    print("\n--- WIND/SWE 2-min comparison ---")
+    if wind is None:
+        print("  No high-quality WIND/SWE fit within ±2 min of the chunk centre.")
+    else:
+        print(
+            f"  WIND/SWE @ {wind['time'].isoformat()}\n"
+            f"    n=({wind['density']:5.3f} ± {wind['density_sigma']:.3f}) cm^-3, "
+            f"T=({wind['temperature']:.3e} ± {wind['temperature_sigma']:.1e}) K, "
+            f"|v|={wind['speed']:6.1f} ± {wind['speed_sigma']:.2f} km/s,\n"
+            f"    v_RTN=[{wind['v_R']:7.2f}±{wind['v_R_sigma']:.2f}, "
+            f"{wind['v_T']:6.2f}±{wind['v_T_sigma']:.2f}, "
+            f"{wind['v_N']:6.2f}±{wind['v_N_sigma']:.2f}]"
+        )
+
+    table_md = _build_intro_table(science_result, coarse_result, boot, wind)
+    _update_doc(table_md)
+    print(f"\nUpdated table block in {_DOC_PATH.relative_to(REPO_ROOT)}")
+
+    _make_plot(
+        epoch_ns,
+        count_rate,
+        esa_voltage,
+        swapi_response,
+        science_result,
+        coarse_result,
+    )
+
+
+# --------------------------------------------------------------------------- #
+# imap-data-access auth + downloads
+# --------------------------------------------------------------------------- #
+
+
+def _download_l2(date_yyyymmdd: str) -> Path:
+    """Find the latest SWAPI L2 file for the given date and download it."""
+    matches = imap_data_access.query(
+        instrument="swapi",
+        data_level="l2",
+        descriptor="sci",
+        start_date=date_yyyymmdd,
+        end_date=date_yyyymmdd,
+        version="latest",
+    )
+    if not matches:
+        raise SystemExit(f"No SWAPI L2 file for {date_yyyymmdd}")
+    file_name = matches[0]["file_path"].split("/")[-1]
+    return imap_data_access.download(file_name)
+
+
+def _furnish_spice_around(date_yyyymmdd: str) -> None:
+    """Download all SPICE kernels covering [D-1, D+2] (and a 90-day lookback
+    for the IMAP SPK so the reconstructed kernel is included), then furnsh.
+
+    Mirrors the two-query strategy in ~/projects/imap-validation/process_swapi_l3.sh.
+    """
+    if spiceypy.ktotal("ALL") > 5:
+        return
+
+    target = datetime.strptime(date_yyyymmdd, "%Y%m%d")
+    start = target - timedelta(days=1)
+    end = target + timedelta(days=2)
+    spk_start = target - timedelta(days=90)
+
+    metakernel_url = (
+        urlparse(imap_data_access.config["DATA_ACCESS_URL"])
+        ._replace(path="metakernel")
+        .geturl()
+    )
+    j2000 = datetime(2000, 1, 1, 12)
+
+    def query_kernels(types, t0, t1):
+        params = {
+            "file_types": [t.value for t in types],
+            "start_time": str(int((t0 - j2000).total_seconds())),
+            "end_time": str(int((t1 - j2000).total_seconds())),
+            "list_files": "true",
+        }
+        resp = requests.get(metakernel_url, params=params, timeout=30)
+        resp.raise_for_status()
+        return [Path(p).name for p in json.loads(resp.text)]
+
+    imap_spk_types = {
+        SpiceKernelTypes.EphemerisReconstructed,
+        SpiceKernelTypes.EphemerisPredicted,
+    }
+    non_spk = [t for t in SpiceKernelTypes if t not in imap_spk_types]
+
+    narrow = query_kernels(non_spk, start, end)
+    spk = query_kernels(list(imap_spk_types), spk_start, end)
+
+    seen = set()
+    kernels = []
+    for k in narrow + spk:
+        if k not in seen:
+            seen.add(k)
+            kernels.append(k)
+    print(f"Downloading {len(kernels)} SPICE kernel(s)…")
+    for k in kernels:
+        path = imap_data_access.download(k)
+        spiceypy.furnsh(str(path))
+
+
+# --------------------------------------------------------------------------- #
+# L2 reading
+# --------------------------------------------------------------------------- #
+
+
+def _read_5_sweep_block(
+    cdf_path: Path, sweep_start: int, n_sweeps: int
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Return (epoch_tt2000_ns, swp_coin_rate, esa_energy) for the 5-sweep slice."""
+    with spacepy.pycdf.CDF(str(cdf_path)) as cdf:
+        epoch_ns = np.asarray(
+            cdf.raw_var("epoch")[sweep_start : sweep_start + n_sweeps], dtype=np.int64
+        )
+        cr = np.asarray(
+            cdf["swp_coin_rate"][sweep_start : sweep_start + n_sweeps, :], dtype=float
+        )
+        esa = np.asarray(
+            cdf["esa_energy"][sweep_start : sweep_start + n_sweeps, :], dtype=float
+        )
+    return epoch_ns, cr, esa
+
+
+def _measurement_times_ns(epoch_ns: np.ndarray, bin_slice: slice) -> np.ndarray:
+    bins = np.arange(bin_slice.start, bin_slice.stop)
+    dt_per_bin = int((12 / 72) * ONE_SECOND_IN_NANOSECONDS)
+    return (epoch_ns[:, None] + bins * dt_per_bin).flatten()
+
+
+# --------------------------------------------------------------------------- #
+# Fitting
+# --------------------------------------------------------------------------- #
+
+
+def _fit_for_bins(
+    bin_slice: slice,
+    count_rate: np.ndarray,
+    esa_voltage: np.ndarray,
+    epoch_ns: np.ndarray,
+    swapi_response,
+):
+    cr = count_rate[:, bin_slice].flatten()
+    voltages = esa_voltage[:, bin_slice].flatten() / SWAPI_L2_K_FACTOR
+    times = _measurement_times_ns(epoch_ns, bin_slice)
+    rotation_matrices = get_swapi_geometry(times)
+    ctx = build_solar_wind_fit_context(
+        count_rate=cr,
+        esa_voltage=voltages,
+        swapi_response=swapi_response,
+        central_effective_area_scale=1.0,
+        rotation_matrices=rotation_matrices,
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    )
+    fit_result = fit_solar_wind_proton_model(ctx)
+    return {
+        "fit_result": fit_result,
+        "ctx": ctx,
+        "bin_slice": bin_slice,
+        "swapi_response": swapi_response,
+    }
+
+
+def _bootstrap_sigmas(
+    result,
+    B: int = 300,
+    xtol: float = 1e-3,
+    seed: int = 42,
+    sc_velocity_rtn: np.ndarray | None = None,
+) -> dict:
+    """Warm-started LM bootstrap (no basin hopping) for σ on (n, T, v_RTN)."""
+    ctx = result["ctx"]
+    fit = result["fit_result"]
+    state0 = SolarWindParams(
+        density=fit.density.nominal_value,
+        bulk_velocity_rtn=np.array([c.nominal_value for c in fit.bulk_velocity_rtn]),
+        temperature=fit.temperature.nominal_value,
+        mass=PROTON_MASS_KG,
+    ).to_vector()
+    cr = ctx.count_rate
+    v = ctx.esa_voltage
+    rm = ctx.rotation_matrices
+    swapi_response_obj = result["swapi_response"]
+
+    rng = np.random.default_rng(seed)
+    fits_n, fits_T, fits_vR, fits_vT, fits_vN = [], [], [], [], []
+    for _ in range(B):
+        idx = rng.choice(len(cr), len(cr), replace=True)
+        ctx_b = build_solar_wind_fit_context(
+            count_rate=cr[idx],
+            esa_voltage=v[idx],
+            swapi_response=swapi_response_obj,
+            central_effective_area_scale=1.0,
+            rotation_matrices=rm[idx],
+            mass_kg=PROTON_MASS_KG,
+            mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+        )
+
+        cache = {}
+
+        def _eval(state):
+            sw = SolarWindParams.from_vector(state, ctx_b.mass_kg)
+            ri, ji = model_solar_wind_ideal_coincidence_rates(sw, ctx_b)
+            df = deadtime_factor(ri)
+            cache["state"] = state.copy()
+            cache["r"] = ri * df - ctx_b.count_rate
+            cache["j"] = ji * np.square(df)[:, None]
+
+        def _residues(state):
+            if "state" not in cache or not np.array_equal(state, cache["state"]):
+                _eval(state)
+            return cache["r"]
+
+        def _jacobian(state):
+            if "state" not in cache or not np.array_equal(state, cache["state"]):
+                _eval(state)
+            return cache["j"]
+
+        try:
+            raw = scipy.optimize.least_squares(
+                _residues,
+                state0.copy(),
+                jac=_jacobian,
+                method="lm",
+                xtol=xtol,
+            )
+        except Exception:
+            continue
+        if not raw.success:
+            continue
+        sw = SolarWindParams.from_vector(raw.x, ctx_b.mass_kg)
+        fits_n.append(sw.density)
+        fits_T.append(sw.temperature)
+        fits_vR.append(float(sw.bulk_velocity_rtn[0]))
+        fits_vT.append(float(sw.bulk_velocity_rtn[1]))
+        fits_vN.append(float(sw.bulk_velocity_rtn[2]))
+
+    arr_n = np.array(fits_n)
+    arr_T = np.array(fits_T)
+    arr_vR = np.array(fits_vR)
+    arr_vT = np.array(fits_vT)
+    arr_vN = np.array(fits_vN)
+    if sc_velocity_rtn is not None and len(arr_n):
+        sun_speed = np.sqrt(
+            (arr_vR + sc_velocity_rtn[0]) ** 2
+            + (arr_vT + sc_velocity_rtn[1]) ** 2
+            + (arr_vN + sc_velocity_rtn[2]) ** 2
+        )
+        sun_speed_sigma = float(np.std(sun_speed, ddof=1))
+    else:
+        sun_speed_sigma = float("nan")
+    return {
+        "B_kept": len(fits_n),
+        "B_total": B,
+        "n_sigma": float(np.std(arr_n, ddof=1)),
+        "T_sigma": float(np.std(arr_T, ddof=1)),
+        "vR_sigma": float(np.std(arr_vR, ddof=1)),
+        "vT_sigma": float(np.std(arr_vT, ddof=1)),
+        "vN_sigma": float(np.std(arr_vN, ddof=1)),
+        "sun_speed_sigma": sun_speed_sigma,
+    }
+
+
+def _build_intro_table(science_result, coarse_result, boot, wind):
+    """Build the markdown table consumed by the Introduction section."""
+    sci_fit = science_result["fit_result"]
+    coa_fit = coarse_result["fit_result"]
+    sci_v_sun = science_result["bulk_velocity_rtn_sun"]
+    coa_v_sun = coarse_result["bulk_velocity_rtn_sun"]
+    # Sun-frame |v| uncertainty: SC velocity is treated as deterministic so
+    # |v_sc + v_sun_offset| inherits std from the SC-frame velocity ufloat.
+    sci_speed_uf = sum(c**2 for c in sci_fit.bulk_velocity_rtn) ** 0.5
+    coa_speed_uf = sum(c**2 for c in coa_fit.bulk_velocity_rtn) ** 0.5
+    sci_speed_sun = float(np.linalg.norm(sci_v_sun))
+    coa_speed_sun = float(np.linalg.norm(coa_v_sun))
+
+    if wind is None:
+        wind_n = wind_T = wind_v = wind_vR = wind_vT = wind_vN = "—"
+        wind_header = "WIND/SWE 2-min (unavailable)"
+    else:
+        wind_t = wind["time"].strftime("%H:%M:%S")
+        wind_header = f"WIND/SWE 2-min @ {wind_t} UT"
+        wind_n = f"{wind['density']:.3f} ± {wind['density_sigma']:.3f}"
+        wind_T = (
+            f"$`({wind['temperature'] / 1e4:.3f} \\pm "
+            f"{wind['temperature_sigma'] / 1e4:.2f})\\times10^{{4}}`$"
+        )
+        wind_v = f"{wind['speed']:.1f} ± {wind['speed_sigma']:.2f}"
+        wind_vR = f"{wind['v_R']:+.2f} ± {wind['v_R_sigma']:.2f}"
+        wind_vT = f"{wind['v_T']:+.2f} ± {wind['v_T_sigma']:.2f}"
+        wind_vN = f"{wind['v_N']:+.2f} ± {wind['v_N_sigma']:.2f}"
+
+    rows = [
+        (
+            "Density $`n`$ (cm⁻³)",
+            f"{sci_fit.density.nominal_value:.3f} ± {sci_fit.density.std_dev:.3f}",
+            f"± {boot['n_sigma']:.3f}",
+            f"{coa_fit.density.nominal_value:.3f} ± {coa_fit.density.std_dev:.3f}",
+            wind_n,
+        ),
+        (
+            "Temperature $`T`$ (K)",
+            f"$`({sci_fit.temperature.nominal_value / 1e4:.3f} \\pm "
+            f"{sci_fit.temperature.std_dev / 1e4:.3f})\\times10^{{4}}`$",
+            f"$`\\pm {boot['T_sigma'] / 1e4:.3f}\\times10^{{4}}`$",
+            f"$`({coa_fit.temperature.nominal_value / 1e4:.3f} \\pm "
+            f"{coa_fit.temperature.std_dev / 1e4:.3f})\\times10^{{4}}`$",
+            wind_T,
+        ),
+        (
+            "Inertial-frame $`\\lvert v \\rvert`$ (km/s)",
+            f"{sci_speed_sun:.1f} ± {sci_speed_uf.std_dev:.2f}",
+            f"± {boot['sun_speed_sigma']:.2f}",
+            f"{coa_speed_sun:.1f} ± {coa_speed_uf.std_dev:.2f}",
+            wind_v,
+        ),
+        (
+            "$`v_{R}`$ (km/s, inertial RTN)",
+            f"{sci_v_sun[0]:.2f} ± {sci_fit.bulk_velocity_rtn[0].std_dev:.2f}",
+            f"± {boot['vR_sigma']:.2f}",
+            f"{coa_v_sun[0]:.2f} ± {coa_fit.bulk_velocity_rtn[0].std_dev:.2f}",
+            wind_vR,
+        ),
+        (
+            "$`v_{T}`$ (km/s, inertial RTN)",
+            f"{sci_v_sun[1]:+.2f} ± {sci_fit.bulk_velocity_rtn[1].std_dev:.2f}",
+            f"± {boot['vT_sigma']:.2f}",
+            f"{coa_v_sun[1]:+.2f} ± {coa_fit.bulk_velocity_rtn[1].std_dev:.2f}",
+            wind_vT,
+        ),
+        (
+            "$`v_{N}`$ (km/s, inertial RTN)",
+            f"{sci_v_sun[2]:+.2f} ± {sci_fit.bulk_velocity_rtn[2].std_dev:.2f}",
+            f"± {boot['vN_sigma']:.2f}",
+            f"{coa_v_sun[2]:+.2f} ± {coa_fit.bulk_velocity_rtn[2].std_dev:.2f}",
+            wind_vN,
+        ),
+    ]
+
+    header_cells = [
+        "Quantity",
+        "SWAPI all bins (HC3 σ)",
+        "SWAPI all bins (boot σ)",
+        "SWAPI coarse only (HC3 σ)",
+        wind_header,
+    ]
+    aligns = ["---", "---:", "---:", "---:", "---:"]
+
+    md = ["| " + " | ".join(header_cells) + " |"]
+    md.append("|" + "|".join(aligns) + "|")
+    for row in rows:
+        md.append("| " + " | ".join(row) + " |")
+    return "\n".join(md)
+
+
+def _update_doc(table_md: str) -> None:
+    text = _DOC_PATH.read_text()
+    begin = text.find(_TABLE_BEGIN)
+    end = text.find(_TABLE_END)
+    if begin < 0 or end < 0 or end <= begin:
+        raise RuntimeError(
+            f"Could not find '{_TABLE_BEGIN}' / '{_TABLE_END}' markers in {_DOC_PATH}"
+        )
+    line_end = text.find("\n", begin) + 1
+    new_text = text[:line_end] + table_md + "\n" + text[end:]
+    _DOC_PATH.write_text(new_text)
+
+
+def _print_fit(result, sc_velocity_rtn: np.ndarray):
+    fit = result["fit_result"]
+    n = fit.density
+    T = fit.temperature
+    v_sc = fit.bulk_velocity_rtn
+    v_sc_nom = np.array([c.nominal_value for c in v_sc])
+    v_sun_nom = v_sc_nom + sc_velocity_rtn
+    # Inertial speed σ ≈ σ on |v_sc| (sc velocity is ~deterministic per epoch).
+    sun_speed_uf = sum(c**2 for c in v_sc) ** 0.5
+    print(
+        f"    n=({n.nominal_value:6.3f} ± {n.std_dev:.3f}) cm^-3, "
+        f"T=({T.nominal_value:.3e} ± {T.std_dev:.1e}) K, "
+        f"|v|_sun={float(np.linalg.norm(v_sun_nom)):6.1f} ± {sun_speed_uf.std_dev:.2f} km/s, "
+        f"v_RTN_sun=[{v_sun_nom[0]:7.2f}±{v_sc[0].std_dev:.2f}, "
+        f"{v_sun_nom[1]:6.2f}±{v_sc[1].std_dev:.2f}, "
+        f"{v_sun_nom[2]:6.2f}±{v_sc[2].std_dev:.2f}], "
+        f"bad_fit={int(fit.bad_fit_flag)}"
+    )
+
+
+def _model_rates_with_deadtime(
+    fit_result, voltages_flat: np.ndarray, rotation_matrices: np.ndarray, swapi_response
+) -> np.ndarray:
+    """Forward-model count rates at the supplied voltages using a fit's params."""
+    swapi_response.warm_cache(voltages_flat)
+    ctx = build_solar_wind_fit_context(
+        count_rate=np.ones_like(voltages_flat),
+        esa_voltage=voltages_flat,
+        swapi_response=swapi_response,
+        central_effective_area_scale=1.0,
+        rotation_matrices=rotation_matrices,
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    )
+    sw = SolarWindParams(
+        density=fit_result.density.nominal_value,
+        bulk_velocity_rtn=np.array(
+            [c.nominal_value for c in fit_result.bulk_velocity_rtn]
+        ),
+        temperature=fit_result.temperature.nominal_value,
+        mass=PROTON_MASS_KG,
+    )
+    rate_ideal, _ = model_solar_wind_ideal_coincidence_rates(sw, ctx)
+    return rate_ideal * deadtime_factor(rate_ideal)
+
+
+# --------------------------------------------------------------------------- #
+# WIND value at chunk centre
+# --------------------------------------------------------------------------- #
+
+
+def _wind_value_at(target_dt: datetime) -> dict | None:
+    """Return WIND/SWE 2-min ground truth nearest to `target_dt` (within ±2 min).
+
+    Layout of the ASCII file matches scripts/swapi/sample_wind_solar_wind.py.
+    """
+    asc = WIND_CACHE_DIR / f"wind_swe_2m_sw{WIND_YEAR}.asc"
+    if not asc.exists():
+        WIND_CACHE_DIR.mkdir(parents=True, exist_ok=True)
+        url = f"{WIND_BASE_URL}/{asc.name}"
+        print(f"Downloading {url} -> {asc}")
+        urllib.request.urlretrieve(url, asc)
+
+    data = np.loadtxt(asc, comments=";")
+
+    fdoy_target = (
+        target_dt - datetime(target_dt.year, 1, 1)
+    ).total_seconds() / 86400.0 + 1.0
+    year = data[:, 0].astype(int)
+    fdoy = data[:, 1]
+    in_year = year == target_dt.year
+    if not in_year.any():
+        return None
+
+    delta = np.abs(fdoy[in_year] - fdoy_target)
+    idx_in_year = int(np.argmin(delta))
+    if delta[idx_in_year] > 2.0 / (60 * 24):  # >2 min
+        return None
+    row = data[in_year][idx_in_year]
+    if int(row[2]) != 10:
+        return None
+    if any(abs(row[c]) >= 9e4 for c in (3, 5, 7, 9, 11, 21)):
+        return None
+
+    # WIND/SWE 2-min ASCII layout: each value is followed by its 1σ uncertainty.
+    # Column indices (0-based): 3 V, 4 σV, 5 Vx, 6 σVx, 7 Vy, 8 σVy, 9 Vz,
+    # 10 σVz, 11 Wscalar, 12 σWscalar, 21 Np, 22 σNp.
+    w_th = row[11]
+    sigma_w = row[12]
+    T_K = (
+        PROTON_MASS_KG
+        * (w_th * 1e3) ** 2
+        / (2.0 * BOLTZMANN_CONSTANT_JOULES_PER_KELVIN)
+    )
+    # Linear-error propagation: T = m·w²/(2k)  ⇒  σT = (2T/w)·σw.
+    sigma_T = T_K * 2.0 * sigma_w / w_th if w_th > 0 else float("nan")
+    return {
+        "time": datetime(target_dt.year, 1, 1) + timedelta(days=row[1] - 1),
+        "speed": float(row[3]),
+        "speed_sigma": float(row[4]),
+        "v_R": float(-row[5]),
+        "v_R_sigma": float(row[6]),
+        "v_T": float(-row[7]),
+        "v_T_sigma": float(row[8]),
+        "v_N": float(row[9]),
+        "v_N_sigma": float(row[10]),
+        "temperature": float(T_K),
+        "temperature_sigma": float(sigma_T),
+        "density": float(row[21]),
+        "density_sigma": float(row[22]),
+    }
+
+
+# --------------------------------------------------------------------------- #
+# Plot
+# --------------------------------------------------------------------------- #
+
+
+def _make_plot(
+    epoch_ns: np.ndarray,
+    count_rate: np.ndarray,
+    esa_voltage: np.ndarray,
+    swapi_response,
+    science_result,
+    coarse_result,
+):
+    voltages = esa_voltage / SWAPI_L2_K_FACTOR
+    valid_full = (voltages > 0) & np.isfinite(voltages)
+    times_full = _measurement_times_ns(epoch_ns, slice(0, 72))
+    rotation_full = get_swapi_geometry(times_full)
+
+    voltages_flat = voltages.flatten()
+    valid_flat = valid_full.flatten()
+    rotation_valid = rotation_full[valid_flat]
+    voltages_valid = voltages_flat[valid_flat]
+
+    science_curve_full = np.full_like(voltages_flat, np.nan)
+    coarse_curve_full = np.full_like(voltages_flat, np.nan)
+    science_curve_full[valid_flat] = _model_rates_with_deadtime(
+        science_result["fit_result"], voltages_valid, rotation_valid, swapi_response
+    )
+    coarse_curve_full[valid_flat] = _model_rates_with_deadtime(
+        coarse_result["fit_result"], voltages_valid, rotation_valid, swapi_response
+    )
+
+    science_2d = science_curve_full.reshape(voltages.shape)
+    coarse_2d = coarse_curve_full.reshape(voltages.shape)
+
+    avg_voltage = np.nanmean(np.where(voltages > 0, voltages, np.nan), axis=0)
+
+    n_sweeps = count_rate.shape[0]
+    n_panels = n_sweeps
+    n_rows = n_panels
+
+    fig = plt.figure(figsize=(10.0, 1.55 * n_rows))
+    outer = fig.add_gridspec(n_rows, 1, hspace=0.0)
+    panel_axes: list[tuple] = []
+    for i in range(n_panels):
+        inner = outer[i, 0].subgridspec(1, 2, width_ratios=[1.4, 4.5], wspace=0)
+        ax_f = fig.add_subplot(inner[0])
+        ax_c = fig.add_subplot(inner[1], sharey=ax_f)
+        panel_axes.append((ax_c, ax_f))
+
+    color_data = "k"
+    color_full = "tab:blue"
+    color_coarse = "tab:orange"
+
+    bin_indices = np.arange(72)
+
+    # Fine sub-axis restricted to V > 100 V: the 3 low-V fine bins (~5–35 V)
+    # plot at effectively zero rate and would introduce a false drop in the line.
+    fine_step_lo = SWAPI_FINE_SWEEP_BINS.start
+    fine_step_hi = SWAPI_FINE_SWEEP_BINS.stop - 1
+    fine_dense_step = np.linspace(fine_step_lo, fine_step_hi, 200)
+
+    def _dense_fine(values_per_bin, v_per_bin):
+        keep = (v_per_bin > 100.0) & np.isfinite(values_per_bin)
+        if keep.sum() < 2:
+            return fine_dense_step, np.full_like(fine_dense_step, np.nan)
+        bins_keep = bin_indices[
+            SWAPI_FINE_SWEEP_BINS.start : SWAPI_FINE_SWEEP_BINS.stop
+        ][keep[SWAPI_FINE_SWEEP_BINS]]
+        order = np.argsort(bins_keep)
+        return (
+            fine_dense_step,
+            np.interp(
+                fine_dense_step,
+                bins_keep[order],
+                values_per_bin[bins_keep[order]],
+                left=np.nan,
+                right=np.nan,
+            ),
+        )
+
+    def _draw(panel, cr, s_curve, c_curve, v, *, draw_legend=False):
+        ax_c, ax_f = panel
+        ok = (v > 0) & np.isfinite(v)
+        coarse_mask = ok & (bin_indices < SWAPI_FINE_SWEEP_BINS.start)
+        fine_mask_useful = (
+            ok & (bin_indices >= SWAPI_FINE_SWEEP_BINS.start) & (v > 100.0)
+        )
+
+        ax_c.semilogy(
+            bin_indices[coarse_mask],
+            np.maximum(cr[coarse_mask], 0.1),
+            "o",
+            color=color_data,
+            ms=3,
+            mfc="none",
+            mew=0.8,
+            label="L2 coarse bin" if draw_legend else None,
+        )
+        ax_f.semilogy(
+            bin_indices[fine_mask_useful],
+            np.maximum(cr[fine_mask_useful], 0.1),
+            "o",
+            color=color_data,
+            ms=4.0,
+            mfc=color_data,
+            mew=0,
+            label="L2 fine-sweep bin" if draw_legend else None,
+        )
+
+        coarse_steps = bin_indices[coarse_mask]
+        for curve, color, ls, label in (
+            (s_curve, color_full, "-", "Fit (bins 1..71, incl. fine sweep)"),
+            (c_curve, color_coarse, "--", "Fit (bins 1..62, no fine sweep)"),
+        ):
+            ax_c.semilogy(
+                coarse_steps,
+                np.maximum(curve[coarse_steps], 0.1),
+                ls,
+                color=color,
+                lw=1.4,
+                label=label if draw_legend else None,
+            )
+            x_dense, y_dense = _dense_fine(curve, v)
+            ax_f.semilogy(
+                x_dense,
+                np.maximum(y_dense, 0.1),
+                ls,
+                color=color,
+                lw=1.4,
+            )
+
+    def _peak_centroid(cr, v, peak_slice: slice):
+        """Sub-bin peak centroid (3-bin count-rate-weighted mean around the
+        brightest bin in `peak_slice`). Returns (bin_centroid, peak_rate)."""
+        bins = np.arange(peak_slice.start, peak_slice.stop)
+        sub_v = v[peak_slice]
+        sub_cr = cr[peak_slice]
+        ok = (sub_v > 0) & np.isfinite(sub_v) & np.isfinite(sub_cr)
+        if ok.sum() < 3:
+            return None, None
+        idx_local = int(np.argmax(np.where(ok, sub_cr, -np.inf)))
+        idx_global = bins[idx_local]
+        lo = max(peak_slice.start, idx_global - 1)
+        hi = min(peak_slice.stop, idx_global + 2)
+        win_bins = np.arange(lo, hi)
+        weights = np.maximum(cr[lo:hi], 0.0)
+        if weights.sum() == 0:
+            return None, None
+        return (
+            float((win_bins * weights).sum() / weights.sum()),
+            float(cr[idx_global]),
+        )
+
+    def _add_peak_markers(panel, cr, v, *, draw_legend=False):
+        """'+' markers at the coarse-bin peak (purple) and fine-bin peak
+        (red), each annotated above the marker with E/q and rate."""
+        ax_c, ax_f = panel
+        bin_grid = np.arange(72, dtype=float)
+        for peak_slice, sub_ax, color, marker_label in (
+            (SWAPI_COARSE_SWEEP_BINS, ax_c, "tab:purple", "Coarse-bin peak"),
+            (SWAPI_FINE_SWEEP_BINS, ax_f, "tab:red", "Fine-bin peak"),
+        ):
+            peak_bin, peak_rate = _peak_centroid(cr, v, peak_slice)
+            if peak_bin is None:
+                continue
+            v_region = v[peak_slice]
+            bins_region = bin_grid[peak_slice]
+            ok_region = (v_region > 0) & np.isfinite(v_region)
+            v_at_peak = float(
+                np.interp(peak_bin, bins_region[ok_region], v_region[ok_region])
+            )
+            e_over_q_eV = SWAPI_K_FACTOR * v_at_peak
+            log10_rate = float(np.log10(max(peak_rate, 1.0)))
+            x_marker = peak_bin
+            sub_ax.plot(
+                [x_marker],
+                [peak_rate],
+                marker="+",
+                color=color,
+                ms=10,
+                mew=1.6,
+                zorder=5,
+                label=marker_label if draw_legend else None,
+            )
+            text_transform = blended_transform_factory(
+                sub_ax.transData, sub_ax.transAxes
+            )
+            sub_ax.text(
+                x_marker,
+                0.98,
+                rf"${e_over_q_eV:.4g}\,\frac{{\mathrm{{eV}}}}{{k^*}},\ "
+                rf"10^{{{log10_rate:.2f}}}\,\mathrm{{Hz}}$",
+                transform=text_transform,
+                color=color,
+                fontsize=10,
+                va="top",
+                ha="center",
+                zorder=5,
+                clip_on=False,
+            )
+
+    panel_titles = [f"Sweep {i + 1}" for i in range(n_sweeps)]
+    panel_data = list(zip(voltages, count_rate, science_2d, coarse_2d))
+    for i, (v, cr, s_curve, c_curve) in enumerate(panel_data):
+        panel = panel_axes[i]
+        _draw(panel, cr, s_curve, c_curve, v, draw_legend=(i == 0))
+        _add_peak_markers(panel, cr, v, draw_legend=(i == 0))
+        panel[1].set_ylabel(f"{panel_titles[i]}\nrate (Hz)", fontsize=9)
+
+    avg_v = np.where(np.isnan(avg_voltage), 0.0, avg_voltage)
+
+    peak = float(np.nanmax(count_rate))
+    last_panel_idx = len(panel_axes) - 1
+    seam_color = "k"
+    for i, (ax_c, ax_f) in enumerate(panel_axes):
+        ax_f.set_xscale("linear")
+        ax_f.set_xlim(fine_step_hi + 0.5, 65.5)
+        ax_c.set_xscale("linear")
+        ax_c.set_xlim(SWAPI_FINE_SWEEP_BINS.start - 0.5, 0.5)
+        ax_f.set_ylim(max(0.1, peak * 1e-5), peak * 60)
+
+        ax_f.spines["right"].set_visible(True)
+        ax_f.spines["right"].set_color(seam_color)
+        ax_f.spines["right"].set_linewidth(1.0)
+        ax_c.spines["left"].set_visible(False)
+        ax_c.tick_params(left=False, labelleft=False)
+        for ax in (ax_f, ax_c):
+            ax.tick_params(labelsize=8)
+            ax.grid(True, which="both", alpha=0.25)
+        is_last = i == last_panel_idx
+        if is_last:
+            ax_f.set_xlabel("Fine Steps", fontsize=10)
+            ax_c.set_xlabel("Coarse Steps", fontsize=10)
+            ax_f.set_xticks([66, 68, 70, 71])
+            ax_f.set_xticklabels(["66", "68", "70", "71"], fontsize=8)
+            ax_c.set_xticks([1, 10, 20, 30, 40, 50, 60])
+            ax_c.set_xticklabels(["1", "10", "20", "30", "40", "50", "60"], fontsize=8)
+            ax_f.tick_params(labelbottom=True, bottom=True)
+            ax_c.tick_params(labelbottom=True, bottom=True)
+        else:
+            ax_f.tick_params(labelbottom=False)
+            ax_c.tick_params(labelbottom=False)
+
+    fine_label_bins = [b for b in [66, 68, 70, 71] if avg_v[b] > 0]
+    coarse_label_bins = [b for b in [1, 5, 10, 20, 30, 40, 50, 60] if avg_v[b] > 0]
+    ax_c_top, ax_f_top = panel_axes[0]
+    top_f = ax_f_top.secondary_xaxis("top")
+    top_f.set_xticks(fine_label_bins)
+    top_f.set_xticklabels([f"{avg_v[b]:.0f}V" for b in fine_label_bins], fontsize=7)
+    top_f.tick_params(labelsize=7, pad=1)
+    top_c = ax_c_top.secondary_xaxis("top")
+    top_c.set_xticks(coarse_label_bins)
+    top_c.set_xticklabels([f"{avg_v[b]:.0f}V" for b in coarse_label_bins], fontsize=7)
+    top_c.set_xlabel("ESA voltage (V)", fontsize=9, labelpad=4)
+    top_c.tick_params(labelsize=7, pad=1)
+
+    handles, labels = panel_axes[0][0].get_legend_handles_labels()
+    handles_f, labels_f = panel_axes[0][1].get_legend_handles_labels()
+    handles += handles_f
+    labels += labels_f
+    fig.legend(
+        handles,
+        labels,
+        loc="lower center",
+        ncol=len(handles),
+        fontsize=9,
+        bbox_to_anchor=(0.5, -0.02),
+    )
+
+    fig.tight_layout(rect=(0.0, 0.05, 1.0, 1.0))
+    FIGURES_DIR.mkdir(parents=True, exist_ok=True)
+    out = FIGURES_DIR / "real_data_fit.svg"
+    fig.savefig(out, bbox_inches="tight")
+    print(f"\nSaved {out}")
+
+
+def _tt2000ns_to_datetime(tt2000_ns: int) -> datetime:
+    """Convert TT2000 nanoseconds to a UTC datetime via spacepy."""
+    return spacepy.pycdf.lib.tt2000_to_datetime(int(tt2000_ns))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/swapi/figure_src/plot_spectra.py b/docs/swapi/figure_src/plot_spectra.py
new file mode 100644
index 000000000..3b2bd58ba
--- /dev/null
+++ b/docs/swapi/figure_src/plot_spectra.py
@@ -0,0 +1,193 @@
+#!/usr/bin/env python3
+"""
+Plot ground-truth vs optimized count-rate spectra for several illustrative SW
+configurations chosen to exercise the integrator's edges:
+  - cold (narrow Maxwellian, dynamic-limit speed window collapses to a sliver)
+  - hot (broad Maxwellian, fills the passband)
+  - bulk elevation past the SG passband edge (per-region elevation clamping)
+  - bulk azimuth straddling the SG/OA boundary (multi-region azimuth split)
+  - fast solar wind (passband shape at high beam energy)
+
+Each panel sweeps ESA voltage across the proton peak and overlays the dynamic-
+limit JIT integrator (calculate_integral) against the fixed-limit, high-resolution
+reference integrator (reference_integral_fixed_limits).
+
+Output: docs/swapi/figures/spectra.svg
+Usage:  python docs/swapi/figure_src/plot_spectra.py
+"""
+
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+from imap_l3_processing.constants import (
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    EV_TO_KELVIN,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    calculate_integral,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
+from scripts.swapi.reference_integral import reference_integral_fixed_limits
+from figure_utils import (
+    FIGURES_DIR,
+    bulk_velocity_rtn_from_swapi_angles,
+    load_swapi_response,
+    peak_esa_voltage_for_proton_bulk_speed,
+)
+
+
+# (label, bulk_speed, T_K, bulk_azimuth, bulk_elevation, density)
+CASES = [
+    (
+        "Nominal: 450 km/s, T = 116,045 K, on-axis",
+        450,
+        10.0 * EV_TO_KELVIN,
+        0.0,
+        0.0,
+        5.0,
+    ),
+    ("Cold: 450 km/s, T = 11,605 K, on-axis", 450, 1.0 * EV_TO_KELVIN, 0.0, 0.0, 5.0),
+    (
+        "Hot: 450 km/s, T = 1,160,452 K, on-axis",
+        450,
+        100.0 * EV_TO_KELVIN,
+        0.0,
+        0.0,
+        5.0,
+    ),
+    (
+        "Off-axis elevation: $\\theta_b = 9\\degree$ (past SG edge)",
+        450,
+        10.0 * EV_TO_KELVIN,
+        0.0,
+        9.0,
+        5.0,
+    ),
+    (
+        "Off-axis azimuth: $\\phi_b = 18\\degree$ (near SG/OA edge)",
+        450,
+        10.0 * EV_TO_KELVIN,
+        18.0,
+        0.0,
+        5.0,
+    ),
+    (
+        "Fast SW: 700 km/s, T = 232,090 K, on-axis",
+        700,
+        20.0 * EV_TO_KELVIN,
+        0.0,
+        0.0,
+        5.0,
+    ),
+]
+
+
+def main():
+    print("Loading calibration data...")
+    swapi_response = load_swapi_response()
+
+    n_voltages = 60
+    fig, axes = plt.subplots(2, 3, figsize=(15, 8))
+
+    rotation_matrix = np.eye(3)
+
+    handles_for_legend = None
+    for ax, (label, v_b, T_k, az, el, density) in zip(axes.flat, CASES):
+        v_peak = peak_esa_voltage_for_proton_bulk_speed(v_b)
+        esa_voltages = np.logspace(
+            np.log10(0.4 * v_peak), np.log10(2.5 * v_peak), n_voltages
+        )
+        sw = SolarWindParams(
+            density=density,
+            bulk_velocity_rtn=bulk_velocity_rtn_from_swapi_angles(v_b, az, el),
+            temperature=T_k,
+            mass=PROTON_MASS_KG,
+        )
+
+        optimized = np.empty(n_voltages)
+        reference = np.empty(n_voltages)
+        swapi_response.warm_cache(esa_voltages)
+        for i, v in enumerate(esa_voltages):
+            response_grid = swapi_response.get_response_grid(
+                float(v), PROTON_MASS_PER_CHARGE_M_P_PER_E, 1.0
+            )
+            rate, _ = calculate_integral(sw, response_grid, rotation_matrix)
+            optimized[i] = rate
+            reference[i] = reference_integral_fixed_limits(
+                response_grid, sw, rotation_matrix
+            )
+
+        (h_ref,) = ax.plot(
+            esa_voltages,
+            reference,
+            "k-",
+            linewidth=2,
+            label="Ground truth (fixed limits)",
+        )
+        (h_prod,) = ax.plot(
+            esa_voltages,
+            optimized,
+            "o",
+            color="tab:orange",
+            markersize=4,
+            markerfacecolor="none",
+            markeredgewidth=1.2,
+            label="Optimized (dynamic limits)",
+        )
+        h_peak = ax.axvline(
+            v_peak,
+            color="gray",
+            linestyle=":",
+            linewidth=0.8,
+            alpha=0.7,
+            label="$v_b$ central voltage",
+        )
+        if handles_for_legend is None:
+            handles_for_legend = [h_ref, h_prod, h_peak]
+
+        ax.set_xscale("log")
+        ax.set_yscale("log")
+        ax.set_xlabel("ESA voltage (V)")
+        ax.set_ylabel("Count rate (Hz)")
+        ax.set_title(label, fontsize=10)
+
+        peak_rate = max(reference.max(), optimized.max(), 1.0)
+        ax.set_ylim(peak_rate * 1e-6, peak_rate * 3)
+        ax.grid(True, which="both", alpha=0.3)
+
+        nz = reference > 1.0
+        if nz.any():
+            rel = np.abs(optimized[nz] - reference[nz]) / reference[nz]
+            print(f"  {label}: max |rel err| above 1 Hz = {rel.max():.2%}")
+
+    fig.suptitle(
+        "Production vs ground-truth count-rate spectra for representative SW configurations",
+        fontsize=13,
+    )
+    fig.legend(
+        handles=handles_for_legend,
+        loc="lower center",
+        ncol=3,
+        bbox_to_anchor=(0.5, -0.01),
+        fontsize=10,
+        frameon=False,
+    )
+    fig.tight_layout(rect=[0, 0.03, 1, 1])
+
+    FIGURES_DIR.mkdir(parents=True, exist_ok=True)
+    out = FIGURES_DIR / "spectra.svg"
+    fig.savefig(out, bbox_inches="tight")
+    print(f"Saved {out}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/swapi/figure_src/plot_uncertainty_mc.py b/docs/swapi/figure_src/plot_uncertainty_mc.py
new file mode 100644
index 000000000..a0d81e173
--- /dev/null
+++ b/docs/swapi/figure_src/plot_uncertainty_mc.py
@@ -0,0 +1,476 @@
+#!/usr/bin/env python3
+"""
+MC validation of the proton-fit Huber–White sandwich uncertainty estimator.
+
+Holds one typical solar-wind ground truth fixed and varies only the Poisson
+seed across many synthetic 5-sweep chunks. The width of the resulting
+distribution of fitted parameters is the *true* sampling sigma; the
+distribution of per-fit estimated sigmas is what the sandwich estimator
+reports. A correctly-calibrated estimator places the histogram of estimated
+sigmas (row 2) at the spread of the histogram of fitted parameters (row 1).
+
+Output: docs/swapi/figures/uncertainty_mc.svg
+Usage:  conda run -n imapL3 python docs/swapi/figure_src/plot_uncertainty_mc.py
+"""
+
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
+
+import types
+
+import numpy as np
+import pandas as pd
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+from imap_l3_processing.constants import (
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
+)
+from imap_l3_processing.swapi.constants import SWAPI_LIVETIME_S
+from figure_utils import (
+    COARSE_BIN_INDICES_IN_SWEEP,
+    COARSE_SWEEP_VOLTAGES_MEAN_V,
+    FIGURES_DIR,
+    compute_per_bin_rotation_matrices,
+    load_swapi_response,
+    run_parallel_map,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.initial_guess import (
+    calculate_initial_guess,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.optimizer import (
+    optimize_solar_wind_params,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.basin_hopping import (
+    escape_local_minimum,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.uncertainties import (
+    derive_uncertainties,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    LOG_DENSITY_IDX,
+    LOG_TEMPERATURE_IDX,
+    VELOCITY_SLICE,
+    SolarWindParams,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    model_solar_wind_ideal_coincidence_rates,
+)
+from imap_l3_processing.swapi.response.deadtime import deadtime_factor
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    build_solar_wind_fit_context,
+)
+
+_N_SWEEPS = 5
+_N_BINS = len(COARSE_SWEEP_VOLTAGES_MEAN_V)
+
+# Typical moderate-speed solar-wind ground truth: density 5 cm^-3, T 1e5 K,
+# bulk +450 km/s radial with small T/N components.
+_TRUTH_DENSITY_CM3 = 5.0
+_TRUTH_TEMPERATURE_K = 1.0e5
+_TRUTH_BULK_RTN_KM_S = np.array([450.0, 5.0, -3.0])
+_N_MC_SAMPLES = 1000
+_LOGNORMAL_REL_SIGMA = 0.01  # 1% relative error in count rates
+# Energy-independent Poisson noise floor (Hz), applied alongside the log-normal
+# multiplicative case. Models a constant detector dark/leakage rate that the
+# proton model does not account for — every bin gets Poisson(floor·τ) counts
+# added on top of the signal.
+_NOISE_FLOOR_HZ = 10.0
+
+_worker_state: types.SimpleNamespace | None = None
+_NOISE_KIND: str = "poisson"
+
+
+def main():
+    _initialize_worker_state()
+    poisson_data = _run_fits(_N_MC_SAMPLES, noise_kind="poisson")
+    poisson_lognormal_floor_data = _run_fits(
+        _N_MC_SAMPLES, noise_kind="poisson+lognormal+floor"
+    )
+    _plot_results(
+        [
+            ("Poisson noise (counts)", poisson_data),
+            (
+                f"Poisson + {_LOGNORMAL_REL_SIGMA:.0%} log-normal "
+                f"+ {_NOISE_FLOOR_HZ:g} Hz floor",
+                poisson_lognormal_floor_data,
+            ),
+        ]
+    )
+
+
+def _initialize_worker_state() -> None:
+    global _worker_state
+
+    swapi_response = load_swapi_response()
+    all_esa_voltages = np.tile(COARSE_SWEEP_VOLTAGES_MEAN_V, _N_SWEEPS)
+    swapi_response.warm_cache(all_esa_voltages)
+    per_bin_rotation_matrices = compute_per_bin_rotation_matrices(
+        _N_SWEEPS, COARSE_BIN_INDICES_IN_SWEEP
+    )
+    base_ctx = build_solar_wind_fit_context(
+        count_rate=np.ones_like(all_esa_voltages),
+        esa_voltage=all_esa_voltages,
+        swapi_response=swapi_response,
+        central_effective_area_scale=1.0,
+        rotation_matrices=per_bin_rotation_matrices,
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    )
+
+    truth_params = SolarWindParams(
+        density=_TRUTH_DENSITY_CM3,
+        bulk_velocity_rtn=_TRUTH_BULK_RTN_KM_S.copy(),
+        temperature=_TRUTH_TEMPERATURE_K,
+        mass=PROTON_MASS_KG,
+    )
+    ideal_rates, _ = model_solar_wind_ideal_coincidence_rates(truth_params, base_ctx)
+    truth_count_rates = ideal_rates * deadtime_factor(ideal_rates)
+
+    _worker_state = types.SimpleNamespace(
+        swapi_response=swapi_response,
+        all_esa_voltages=all_esa_voltages,
+        per_bin_rotation_matrices=per_bin_rotation_matrices,
+        truth_count_rates=truth_count_rates,
+    )
+
+
+def _run_fits(n_samples: int, noise_kind: str) -> pd.DataFrame:
+    global _NOISE_KIND
+    _NOISE_KIND = noise_kind
+    rows = run_parallel_map(
+        _process_one, n_samples, desc=f"mc-{noise_kind}", chunksize=10
+    )
+    data = pd.DataFrame(rows)
+    print(f"Bad-fit flags: {data['bad_flag'].sum()}/{n_samples}")
+    return data
+
+
+def _process_one(i):
+    ws = _worker_state
+    rng = np.random.default_rng(i)
+    counts = rng.poisson(np.maximum(ws.truth_count_rates * SWAPI_LIVETIME_S, 0.0))
+    count_rates = counts.astype(float) / SWAPI_LIVETIME_S
+    if _NOISE_KIND == "poisson":
+        pass
+    elif _NOISE_KIND == "poisson+lognormal+floor":
+        # Multiplicative log-normal noise on top of Poisson sampling, with
+        # std-dev ≈ _LOGNORMAL_REL_SIGMA in linear space. Mean-1 correction:
+        # shift by -σ²/2 in log space.
+        sigma_log = np.sqrt(np.log1p(_LOGNORMAL_REL_SIGMA**2))
+        log_factor = rng.normal(-0.5 * sigma_log**2, sigma_log, count_rates.shape)
+        count_rates = np.maximum(count_rates * np.exp(log_factor), 0.0)
+        # Energy-independent Poisson noise floor: every bin gets Poisson(floor·τ)
+        # counts added — a stand-in for unmodelled detector dark/leakage rate.
+        # The mean adds 10 Hz to obs at every bin; the variance is √(floor·τ)/τ
+        # ≈ 8 Hz per bin. The fit's model does not account for this.
+        floor_counts = rng.poisson(
+            _NOISE_FLOOR_HZ * SWAPI_LIVETIME_S, size=count_rates.shape
+        )
+        count_rates = count_rates + floor_counts.astype(float) / SWAPI_LIVETIME_S
+    else:
+        raise ValueError(f"unknown noise_kind: {_NOISE_KIND}")
+
+    fit_ctx = build_solar_wind_fit_context(
+        count_rate=count_rates,
+        esa_voltage=ws.all_esa_voltages,
+        swapi_response=ws.swapi_response,
+        central_effective_area_scale=1.0,
+        rotation_matrices=ws.per_bin_rotation_matrices,
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    )
+    sigma_keys = [
+        "fit_density_sigma",
+        "fit_temperature_sigma",
+        "fit_radial_speed_sigma",
+        "fit_tangential_speed_sigma",
+        "fit_normal_speed_sigma",
+    ]
+    nan_row = {
+        k: float("nan")
+        for k in [
+            "fit_density",
+            "fit_temperature",
+            "fit_radial_speed",
+            "fit_tangential_speed",
+            "fit_normal_speed",
+            *sigma_keys,
+            *(f"ols_{k}" for k in sigma_keys),
+        ]
+    } | {"bad_flag": True}
+    try:
+        initial_guess = calculate_initial_guess(fit_ctx)
+        first_result = optimize_solar_wind_params(initial_guess, fit_ctx)
+        final_result = escape_local_minimum(first_result, fit_ctx)
+    except Exception as e:
+        print(f"  case {i}: fit failed ({type(e).__name__}: {e})")
+        return nan_row
+
+    sw = final_result.sw_params
+    sandwich_n_sigma, sandwich_T_sigma, sandwich_v_cov = derive_uncertainties(
+        final_result, fit_ctx
+    )
+    ols_n_sigma, ols_T_sigma, ols_v_sigma = _uncorrected_sigmas(final_result)
+    sandwich_v_sigma = np.sqrt(np.maximum(np.diag(sandwich_v_cov), 0.0))
+
+    return {
+        "fit_density": sw.density,
+        "fit_temperature": sw.temperature,
+        "fit_radial_speed": float(sw.bulk_velocity_rtn[0]),
+        "fit_tangential_speed": float(sw.bulk_velocity_rtn[1]),
+        "fit_normal_speed": float(sw.bulk_velocity_rtn[2]),
+        "fit_density_sigma": sandwich_n_sigma,
+        "fit_temperature_sigma": sandwich_T_sigma,
+        "fit_radial_speed_sigma": float(sandwich_v_sigma[0]),
+        "fit_tangential_speed_sigma": float(sandwich_v_sigma[1]),
+        "fit_normal_speed_sigma": float(sandwich_v_sigma[2]),
+        "ols_fit_density_sigma": ols_n_sigma,
+        "ols_fit_temperature_sigma": ols_T_sigma,
+        "ols_fit_radial_speed_sigma": float(ols_v_sigma[0]),
+        "ols_fit_tangential_speed_sigma": float(ols_v_sigma[1]),
+        "ols_fit_normal_speed_sigma": float(ols_v_sigma[2]),
+        "bad_flag": bool(not final_result.success),
+    }
+
+
+def _uncorrected_sigmas(opt_result) -> tuple[float, float, np.ndarray]:
+    """Previous uncorrected formula: σ² = (Σr²/(N − K)) · pinv(JᵀJ). Assumes
+    homoscedastic residuals; replaced by the Huber–White sandwich. Reproduced
+    here for MC comparison only."""
+    J = opt_result.jacobian
+    n_params = J.shape[1]
+    n_obs = len(opt_result.residuals)
+    residual_variance = float(np.sum(opt_result.residuals**2)) / max(
+        n_obs - n_params, 1
+    )
+    try:
+        cov = residual_variance * np.linalg.pinv(J.T @ J)
+    except np.linalg.LinAlgError:
+        return float("nan"), float("nan"), np.full(3, float("nan"))
+    log_n_var = cov[LOG_DENSITY_IDX, LOG_DENSITY_IDX]
+    log_T_var = cov[LOG_TEMPERATURE_IDX, LOG_TEMPERATURE_IDX]
+    velocity_cov = cov[VELOCITY_SLICE, VELOCITY_SLICE]
+    sw = opt_result.sw_params
+    return (
+        float(sw.density * np.sqrt(max(log_n_var, 0.0))),
+        float(sw.temperature * np.sqrt(max(log_T_var, 0.0))),
+        np.sqrt(np.maximum(np.diag(velocity_cov), 0.0)),
+    )
+
+
+def _plot_results(experiments: list[tuple[str, pd.DataFrame]]) -> None:
+    columns = [
+        (
+            "Density (cm⁻³)",
+            "fit_density",
+            "fit_density_sigma",
+            "ols_fit_density_sigma",
+            _TRUTH_DENSITY_CM3,
+        ),
+        (
+            "Temperature (K)",
+            "fit_temperature",
+            "fit_temperature_sigma",
+            "ols_fit_temperature_sigma",
+            _TRUTH_TEMPERATURE_K,
+        ),
+        (
+            "$v_R$ (km/s)",
+            "fit_radial_speed",
+            "fit_radial_speed_sigma",
+            "ols_fit_radial_speed_sigma",
+            _TRUTH_BULK_RTN_KM_S[0],
+        ),
+        (
+            "$v_T$ (km/s)",
+            "fit_tangential_speed",
+            "fit_tangential_speed_sigma",
+            "ols_fit_tangential_speed_sigma",
+            _TRUTH_BULK_RTN_KM_S[1],
+        ),
+        (
+            "$v_N$ (km/s)",
+            "fit_normal_speed",
+            "fit_normal_speed_sigma",
+            "ols_fit_normal_speed_sigma",
+            _TRUTH_BULK_RTN_KM_S[2],
+        ),
+    ]
+
+    n_experiments = len(experiments)
+    fig, axes = plt.subplots(2 * n_experiments, 5, figsize=(17, 6.5 * n_experiments))
+    if n_experiments == 1:
+        axes = np.array([axes[0], axes[1]]).reshape(2, 5)
+    fig.suptitle(
+        f"Sandwich-estimator MC validation: one truth\n"
+        f"(n={_TRUTH_DENSITY_CM3:g} cm⁻³, T={_TRUTH_TEMPERATURE_K:.0e} K, "
+        f"v_RTN=({_TRUTH_BULK_RTN_KM_S[0]:g}, {_TRUTH_BULK_RTN_KM_S[1]:g}, "
+        f"{_TRUTH_BULK_RTN_KM_S[2]:g}) km/s, "
+        f"{_N_SWEEPS} sweeps × {_N_BINS} coarse bins)",
+        fontsize=11,
+    )
+
+    color_fits = "tab:blue"
+    color_sandwich = "tab:orange"
+    color_ols = "tab:green"
+
+    for exp_idx, (exp_label, data) in enumerate(experiments):
+        good = ~data["bad_flag"]
+        n_good = int(good.sum())
+        row_top = 2 * exp_idx
+        row_bot = 2 * exp_idx + 1
+        # Group label on the left margin
+        fig.text(
+            0.005,
+            1.0 - (exp_idx + 0.5) / n_experiments,
+            f"{exp_label}\n(n={n_good} MC samples)",
+            ha="left",
+            va="center",
+            fontsize=10,
+            rotation=90,
+            weight="bold",
+        )
+
+        for col_idx, (
+            label,
+            fit_key,
+            sigma_key,
+            ols_sigma_key,
+            truth,
+        ) in enumerate(columns):
+            fits = data.loc[good, fit_key].to_numpy()
+            sigmas = data.loc[good, sigma_key].to_numpy()
+            ols_sigmas = data.loc[good, ols_sigma_key].to_numpy()
+            empirical_sigma = float(np.nanstd(fits, ddof=1))
+            bias = float(np.nanmean(fits)) - truth
+
+            ax_top = axes[row_top, col_idx]
+            ax_top.hist(
+                fits,
+                bins=40,
+                color=color_fits,
+                alpha=0.75,
+                edgecolor="white",
+                rasterized=True,
+            )
+            ax_top.axvline(truth, color="k", lw=1.0, ls="--", alpha=0.7, label="truth")
+            ax_top.set_title(label, fontsize=10)
+            ax_top.set_xlabel(f"Fitted {label}", fontsize=9)
+            ax_top.set_ylabel("MC count", fontsize=9)
+            ax_top.tick_params(labelsize=8)
+            ax_top.grid(True, alpha=0.25)
+            ax_top.annotate(
+                f"empirical σ = {_format_sigma(empirical_sigma, label)}\n"
+                f"bias (mean − truth) = {_format_signed_sigma(bias, label)} "
+                f"({bias / empirical_sigma:+.2f} σ)",
+                xy=(0.04, 0.97),
+                xycoords="axes fraction",
+                va="top",
+                ha="left",
+                fontsize=7.5,
+                bbox=dict(boxstyle="round,pad=0.3", fc="white", alpha=0.8),
+            )
+            if col_idx == 0:
+                ax_top.legend(fontsize=7, loc="upper right", framealpha=0.8)
+
+            # Bottom row: histograms of normalized residuals (fit − truth) / σ̂.
+            # A well-calibrated σ̂ estimator gives a unit Gaussian; a σ̂ that
+            # understates the true scatter by factor k gives a Gaussian of
+            # width k. Tells the reader at a glance whether the bars are right.
+            ax_bot = axes[row_bot, col_idx]
+            normalized_resid_sandwich = (fits - truth) / sigmas
+            normalized_resid_ols = (fits - truth) / ols_sigmas
+            finite_sw = normalized_resid_sandwich[
+                np.isfinite(normalized_resid_sandwich)
+            ]
+            finite_ols_n = normalized_resid_ols[np.isfinite(normalized_resid_ols)]
+            x_max = float(np.nanmax(np.abs(np.concatenate([finite_sw, finite_ols_n]))))
+            x_max = max(x_max, 4.0)  # always show at least ±4σ
+            bins_norm = np.linspace(-x_max, x_max, 51)
+            ax_bot.hist(
+                normalized_resid_ols,
+                bins=bins_norm,
+                color=color_ols,
+                alpha=0.6,
+                edgecolor="white",
+                label="Uncorrected",
+                rasterized=True,
+            )
+            ax_bot.hist(
+                normalized_resid_sandwich,
+                bins=bins_norm,
+                color=color_sandwich,
+                alpha=0.75,
+                edgecolor="white",
+                label="HC3 sandwich",
+                rasterized=True,
+            )
+            # Reference: the unit-σ Gaussian a calibrated estimator should produce.
+            n_good_finite = max(len(finite_sw), 1)
+            bin_width = bins_norm[1] - bins_norm[0]
+            x_ref = np.linspace(-x_max, x_max, 200)
+            unit_gaussian_pdf = (
+                (np.exp(-0.5 * x_ref**2) / np.sqrt(2 * np.pi))
+                * n_good_finite
+                * bin_width
+            )
+            ax_bot.plot(
+                x_ref,
+                unit_gaussian_pdf,
+                color="k",
+                lw=1.2,
+                ls="--",
+                alpha=0.7,
+                label="N(0, 1) (calibrated)",
+            )
+            sandwich_calibration = float(np.nanstd(finite_sw, ddof=1))
+            ols_calibration = float(np.nanstd(finite_ols_n, ddof=1))
+            ax_bot.set_xlabel(f"(fit − truth) / σ̂ for {label}", fontsize=9)
+            ax_bot.set_ylabel("MC count", fontsize=9)
+            ax_bot.tick_params(labelsize=8)
+            ax_bot.grid(True, alpha=0.25)
+            ax_bot.annotate(
+                f"std of (fit − truth)/σ̂_HC3 = {sandwich_calibration:.2f}\n"
+                f"std of (fit − truth)/σ̂_uncorrected = {ols_calibration:.2f}",
+                xy=(0.04, 0.97),
+                xycoords="axes fraction",
+                va="top",
+                ha="left",
+                fontsize=7.5,
+                bbox=dict(boxstyle="round,pad=0.3", fc="white", alpha=0.8),
+            )
+            if col_idx == 0:
+                ax_bot.legend(fontsize=7, loc="upper right", framealpha=0.8)
+
+    fig.tight_layout(rect=(0.03, 0.0, 1.0, 1.0))
+
+    FIGURES_DIR.mkdir(parents=True, exist_ok=True)
+    out_path = FIGURES_DIR / "uncertainty_mc.svg"
+    fig.savefig(out_path, bbox_inches="tight", dpi=200)
+    print(f"Saved {out_path}")
+
+
+def _format_sigma(value: float, label: str) -> str:
+    if "Density" in label:
+        return f"{value:.3f} cm⁻³"
+    if "Temperature" in label:
+        return f"{value:.2e} K"
+    return f"{value:.2f} km/s"
+
+
+def _format_signed_sigma(value: float, label: str) -> str:
+    if "Density" in label:
+        return f"{value:+.3f} cm⁻³"
+    if "Temperature" in label:
+        return f"{value:+.2e} K"
+    return f"{value:+.2f} km/s"
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/swapi/figure_src/wind_solar_wind_samples_2025.csv b/docs/swapi/figure_src/wind_solar_wind_samples_2025.csv
new file mode 100644
index 000000000..447e2e05f
--- /dev/null
+++ b/docs/swapi/figure_src/wind_solar_wind_samples_2025.csv
@@ -0,0 +1,10001 @@
+year,fdoy,fit_flag,proton_bulk_speed_km_s,proton_thermal_speed_km_s,proton_density_cm3,proton_vx_gse_km_s,proton_vy_gse_km_s,proton_vz_gse_km_s,v_R_km_s,v_T_km_s,v_N_km_s,proton_temperature_K
+2025,1.115667,10,465.500,50.500,19.7490,-464.600,25.100,16.100,464.600,-25.100,16.100,1.545e+05
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+2025,1.134292,10,463.100,44.900,29.2220,-462.000,23.700,21.200,462.000,-23.700,21.200,1.221e+05
+2025,1.145860,10,483.700,44.400,20.3660,-483.500,8.800,-7.700,483.500,-8.800,-7.700,1.194e+05
+2025,1.172596,10,486.200,51.600,36.9210,-484.600,37.100,-13.200,484.600,-37.100,-13.200,1.613e+05
+2025,1.174924,10,483.600,52.900,37.2960,-481.900,40.300,-5.600,481.900,-40.300,-5.600,1.695e+05
+2025,1.181908,10,489.700,50.500,35.4140,-489.000,23.300,-11.600,489.000,-23.300,-11.600,1.545e+05
+2025,1.207408,10,503.700,40.900,23.4970,-503.000,11.800,-23.800,503.000,-11.800,-23.800,1.013e+05
+2025,1.250477,10,509.000,27.900,9.5350,-507.000,-8.300,-43.300,507.000,8.300,-43.300,4.715e+04
+2025,1.273758,10,527.500,37.500,19.4520,-526.900,16.500,-19.700,526.900,-16.500,-19.700,8.518e+04
+2025,1.379540,10,509.900,57.800,17.2160,-509.000,16.200,-26.400,509.000,-16.200,-26.400,2.024e+05
+2025,1.435341,10,521.900,21.100,21.4980,-516.000,38.100,-67.900,516.000,-38.100,-67.900,2.697e+04
+2025,1.436505,10,522.700,22.900,24.5330,-518.700,28.000,-58.400,518.700,-28.000,-58.400,3.177e+04
+2025,1.447018,10,524.600,22.100,21.6700,-522.100,17.000,-48.500,522.100,-17.000,-48.500,2.958e+04
+2025,1.453965,10,519.900,23.500,23.7030,-516.900,14.500,-53.900,516.900,-14.500,-53.900,3.345e+04
+2025,1.456293,10,521.000,25.900,30.2540,-518.200,14.300,-52.700,518.200,-14.300,-52.700,4.063e+04
+2025,1.459786,10,526.200,32.500,31.6900,-524.200,24.200,-39.000,524.200,-24.200,-39.000,6.398e+04
+2025,1.464442,10,532.800,33.600,30.7720,-530.900,23.800,-38.400,530.900,-23.800,-38.400,6.839e+04
+2025,1.465606,10,551.400,23.900,24.8800,-550.700,7.000,-26.800,550.700,-7.000,-26.800,3.460e+04
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+2025,2.679187,10,485.200,22.300,9.0380,-484.200,19.300,-25.300,484.200,-19.300,-25.300,3.012e+04
+2025,2.691991,10,491.000,19.100,9.7220,-490.000,25.300,-18.600,490.000,-25.300,-18.600,2.210e+04
+2025,2.709525,10,534.700,21.500,7.7020,-534.300,8.100,18.000,534.300,-8.100,18.000,2.800e+04
+2025,2.710689,10,527.200,18.100,4.8230,-527.000,1.300,14.800,527.000,-1.300,14.800,1.984e+04
+2025,2.737425,10,492.400,23.300,5.2920,-492.000,-10.100,-15.700,492.000,10.100,-15.700,3.288e+04
+2025,2.742045,10,489.500,23.100,7.3670,-489.000,-13.100,-16.000,489.000,13.100,-16.000,3.232e+04
+2025,2.758305,10,490.100,23.700,7.5020,-489.500,-13.000,-20.100,489.500,13.000,-20.100,3.402e+04
+2025,2.774565,10,486.600,24.500,6.3150,-486.200,-13.500,-13.700,486.200,13.500,-13.700,3.636e+04
+2025,2.787334,10,491.300,21.000,5.9270,-491.100,-12.100,-6.700,491.100,12.100,-6.700,2.671e+04
+2025,2.794318,10,487.000,23.500,6.2580,-486.500,-18.900,-11.800,486.500,18.900,-11.800,3.345e+04
+2025,2.812979,10,485.900,21.500,5.7870,-485.500,-12.700,-12.200,485.500,12.700,-12.200,2.800e+04
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+      <use xlink:href="#DejaVuSans-6e" transform="translate(29.492188 0)"/>
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+      <!-- Init  RMSE = 49.1 km/s -->
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+      <!-- Fit    RMSE = 0.6 km/s -->
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+       <use xlink:href="#DejaVuSans-46"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(50.269531 0)"/>
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+       <use xlink:href="#DejaVuSans-6d" transform="translate(922.925781 0)"/>
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+     <g id="text_56">
+      <!-- $v_T$ (km/s) -->
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+     <g id="line2d_51">
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+      </g>
+     </g>
+     <g id="text_31">
+      <!-- 60 -->
+      <g transform="translate(302.718 79.158577) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-36"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_16">
+     <g id="line2d_53">
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+     <g id="line2d_54">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="319.898" y="52.886788" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_32">
+      <!-- 70 -->
+      <g transform="translate(302.718 55.926163) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-37"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_33">
+     <!-- MC count -->
+     <g transform="translate(296.846281 153.724672) rotate(-90) scale(0.09 -0.09)">
+      <use xlink:href="#DejaVuSans-4d"/>
+      <use xlink:href="#DejaVuSans-43" transform="translate(86.279297 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(156.103516 0)"/>
+      <use xlink:href="#DejaVuSans-63" transform="translate(187.890625 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(242.871094 0)"/>
+      <use xlink:href="#DejaVuSans-75" transform="translate(304.052734 0)"/>
+      <use xlink:href="#DejaVuSans-6e" transform="translate(367.431641 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(430.810547 0)"/>
+     </g>
+    </g>
+   </g>
+   <g id="line2d_55">
+    <path d="M 406.690299 215.513688 
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+   </g>
+   <g id="patch_10">
+    <path d="M 319.898 215.513688 
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+   </g>
+   <g id="patch_11">
+    <path d="M 497.425655 215.513688 
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+   </g>
+   <g id="patch_12">
+    <path d="M 319.898 215.513688 
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+   </g>
+   <g id="patch_13">
+    <path d="M 319.898 49.63425 
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+   </g>
+   <g id="text_34">
+    <g id="patch_14">
+     <path d="M 326.999106 72.517586 
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+    </g>
+    <!-- empirical σ = 1.14e+03 K -->
+    <g transform="translate(326.999106 60.309461) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-65"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(61.523438 0)"/>
+     <use xlink:href="#DejaVuSans-70" transform="translate(158.935547 0)"/>
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+     <use xlink:href="#DejaVuSans-72" transform="translate(250.195312 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(291.308594 0)"/>
+     <use xlink:href="#DejaVuSans-63" transform="translate(319.091797 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(374.072266 0)"/>
+     <use xlink:href="#DejaVuSans-6c" transform="translate(435.351562 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(463.134766 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(494.921875 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(558.300781 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(590.087891 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(673.876953 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(705.664062 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(769.287109 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(801.074219 0)"/>
+     <use xlink:href="#DejaVuSans-34" transform="translate(864.697266 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(928.320312 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(989.84375 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1073.632812 0)"/>
+     <use xlink:href="#DejaVuSans-33" transform="translate(1137.255859 0)"/>
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+     <use xlink:href="#DejaVuSans-4b" transform="translate(1232.666016 0)"/>
+    </g>
+    <!-- bias (mean − truth) = +1.21e+01 K (+0.01 σ) -->
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+     <use xlink:href="#DejaVuSans-62"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(63.476562 0)"/>
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+     <use xlink:href="#DejaVuSans-20" transform="translate(674.609375 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(706.396484 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(745.605469 0)"/>
+     <use xlink:href="#DejaVuSans-75" transform="translate(786.71875 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(850.097656 0)"/>
+     <use xlink:href="#DejaVuSans-68" transform="translate(889.306641 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(952.685547 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(991.699219 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(1023.486328 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1107.275391 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1139.0625 0)"/>
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+     <use xlink:href="#DejaVuSans-2e" transform="translate(1286.474609 0)"/>
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+     <use xlink:href="#DejaVuSans-65" transform="translate(1445.507812 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1507.03125 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1590.820312 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(1654.443359 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1718.066406 0)"/>
+     <use xlink:href="#DejaVuSans-4b" transform="translate(1749.853516 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1815.429688 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(1847.216797 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1886.230469 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1970.019531 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(2033.642578 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(2065.429688 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(2129.052734 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(2192.675781 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(2224.462891 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(2287.841797 0)"/>
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+   </g>
+   <g id="text_35">
+    <!-- Temperature (K) -->
+    <g transform="translate(368.222765 43.63425) scale(0.1 -0.1)">
+     <use xlink:href="#DejaVuSans-54"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(44.083984 0)"/>
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+     <use xlink:href="#DejaVuSans-75" transform="translate(469.621094 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(533 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(571.863281 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(633.386719 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(665.173828 0)"/>
+     <use xlink:href="#DejaVuSans-4b" transform="translate(704.1875 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(769.763672 0)"/>
+    </g>
+   </g>
+  </g>
+  <g id="axes_3">
+   <g id="patch_15">
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+      <g>
+       <use xlink:href="#m75fa9cbc46" x="555.626" y="141.460367" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_45">
+      <!-- 30 -->
+      <g transform="translate(538.446 144.499742) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-33"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_21">
+     <g id="line2d_74">
+      <path d="M 555.626 116.775927 
+L 733.153655 116.775927 
+" clip-path="url(#p7b87b288f2)" style="fill: none; stroke: #b0b0b0; stroke-opacity: 0.25; stroke-width: 0.8; stroke-linecap: square"/>
+     </g>
+     <g id="line2d_75">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="555.626" y="116.775927" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_46">
+      <!-- 40 -->
+      <g transform="translate(538.446 119.815302) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_22">
+     <g id="line2d_76">
+      <path d="M 555.626 92.091487 
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+     </g>
+     <g id="line2d_77">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="555.626" y="92.091487" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_47">
+      <!-- 50 -->
+      <g transform="translate(538.446 95.130862) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_23">
+     <g id="line2d_78">
+      <path d="M 555.626 67.407047 
+L 733.153655 67.407047 
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+     </g>
+     <g id="line2d_79">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="555.626" y="67.407047" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_48">
+      <!-- 60 -->
+      <g transform="translate(538.446 70.446422) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-36"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_49">
+     <!-- MC count -->
+     <g transform="translate(532.574281 153.724672) rotate(-90) scale(0.09 -0.09)">
+      <use xlink:href="#DejaVuSans-4d"/>
+      <use xlink:href="#DejaVuSans-43" transform="translate(86.279297 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(156.103516 0)"/>
+      <use xlink:href="#DejaVuSans-63" transform="translate(187.890625 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(242.871094 0)"/>
+      <use xlink:href="#DejaVuSans-75" transform="translate(304.052734 0)"/>
+      <use xlink:href="#DejaVuSans-6e" transform="translate(367.431641 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(430.810547 0)"/>
+     </g>
+    </g>
+   </g>
+   <g id="line2d_80">
+    <path d="M 643.125401 215.513688 
+L 643.125401 49.63425 
+" clip-path="url(#p7b87b288f2)" style="fill: none; stroke-dasharray: 3.7,1.6; stroke-dashoffset: 0; stroke: #000000; stroke-opacity: 0.7"/>
+   </g>
+   <g id="patch_16">
+    <path d="M 555.626 215.513688 
+L 555.626 49.63425 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="patch_17">
+    <path d="M 733.153655 215.513688 
+L 733.153655 49.63425 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="patch_18">
+    <path d="M 555.626 215.513688 
+L 733.153655 215.513688 
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+   </g>
+   <g id="patch_19">
+    <path d="M 555.626 49.63425 
+L 733.153655 49.63425 
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+   </g>
+   <g id="text_50">
+    <g id="patch_20">
+     <path d="M 562.727106 72.517586 
+L 722.802887 72.517586 
+Q 725.052887 72.517586 725.052887 70.267586 
+L 725.052887 54.610633 
+Q 725.052887 52.360633 722.802887 52.360633 
+L 562.727106 52.360633 
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+L 560.477106 70.267586 
+Q 560.477106 72.517586 562.727106 72.517586 
+z
+" style="fill: #ffffff; opacity: 0.8; stroke: #000000; stroke-linejoin: miter"/>
+    </g>
+    <!-- empirical σ = 0.18 km/s -->
+    <g transform="translate(562.727106 60.309461) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-65"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(61.523438 0)"/>
+     <use xlink:href="#DejaVuSans-70" transform="translate(158.935547 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(222.412109 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(250.195312 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(291.308594 0)"/>
+     <use xlink:href="#DejaVuSans-63" transform="translate(319.091797 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(374.072266 0)"/>
+     <use xlink:href="#DejaVuSans-6c" transform="translate(435.351562 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(463.134766 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(494.921875 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(558.300781 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(590.087891 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(673.876953 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(705.664062 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(769.287109 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(801.074219 0)"/>
+     <use xlink:href="#DejaVuSans-38" transform="translate(864.697266 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(928.320312 0)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(960.107422 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1018.017578 0)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(1115.429688 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(1149.121094 0)"/>
+    </g>
+    <!-- bias (mean − truth) = -0.00 km/s (-0.01 σ) -->
+    <g transform="translate(562.727106 68.707821) scale(0.075 -0.075)">
+     <defs>
+      <path id="DejaVuSans-2d" d="M 313 2009 
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+L 1997 1497 
+L 313 1497 
+L 313 2009 
+z
+" transform="scale(0.015625)"/>
+     </defs>
+     <use xlink:href="#DejaVuSans-62"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(63.476562 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(91.259766 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(152.539062 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(204.638672 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(236.425781 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(275.439453 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(372.851562 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(434.375 0)"/>
+     <use xlink:href="#DejaVuSans-6e" transform="translate(495.654297 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(559.033203 0)"/>
+     <use xlink:href="#DejaVuSans-2212" transform="translate(590.820312 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(674.609375 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(706.396484 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(745.605469 0)"/>
+     <use xlink:href="#DejaVuSans-75" transform="translate(786.71875 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(850.097656 0)"/>
+     <use xlink:href="#DejaVuSans-68" transform="translate(889.306641 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(952.685547 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(991.699219 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(1023.486328 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1107.275391 0)"/>
+     <use xlink:href="#DejaVuSans-2d" transform="translate(1139.0625 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1175.146484 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(1238.769531 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1270.556641 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1334.179688 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1397.802734 0)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(1429.589844 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1487.5 0)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(1584.912109 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(1618.603516 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1670.703125 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(1702.490234 0)"/>
+     <use xlink:href="#DejaVuSans-2d" transform="translate(1741.503906 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1777.587891 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(1841.210938 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1872.998047 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(1936.621094 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(2000.244141 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(2032.03125 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(2095.410156 0)"/>
+    </g>
+   </g>
+   <g id="text_51">
+    <!-- $v_R$ (km/s) -->
+    <g transform="translate(621.289828 43.63425) scale(0.1 -0.1)">
+     <use xlink:href="#DejaVuSans-Oblique-76" transform="translate(0 0.015625)"/>
+     <use xlink:href="#DejaVuSans-Oblique-52" transform="translate(59.179688 -16.390625) scale(0.7)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(110.551758 0.015625)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(142.338867 0.015625)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(181.352539 0.015625)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(239.262695 0.015625)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(336.674805 0.015625)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(370.366211 0.015625)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(422.46582 0.015625)"/>
+    </g>
+   </g>
+  </g>
+  <g id="axes_4">
+   <g id="patch_21">
+    <path d="M 791.354 215.513688 
+L 968.881655 215.513688 
+L 968.881655 49.63425 
+L 791.354 49.63425 
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+" style="fill: #ffffff"/>
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+     </g>
+     <g id="line2d_106">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="791.354" y="64.025617" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_65">
+      <!-- 70 -->
+      <g transform="translate(774.174 67.064992) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-37"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_66">
+     <!-- MC count -->
+     <g transform="translate(768.302281 153.724672) rotate(-90) scale(0.09 -0.09)">
+      <use xlink:href="#DejaVuSans-4d"/>
+      <use xlink:href="#DejaVuSans-43" transform="translate(86.279297 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(156.103516 0)"/>
+      <use xlink:href="#DejaVuSans-63" transform="translate(187.890625 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(242.871094 0)"/>
+      <use xlink:href="#DejaVuSans-75" transform="translate(304.052734 0)"/>
+      <use xlink:href="#DejaVuSans-6e" transform="translate(367.431641 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(430.810547 0)"/>
+     </g>
+    </g>
+   </g>
+   <g id="line2d_107">
+    <path d="M 893.730289 215.513688 
+L 893.730289 49.63425 
+" clip-path="url(#p221070b906)" style="fill: none; stroke-dasharray: 3.7,1.6; stroke-dashoffset: 0; stroke: #000000; stroke-opacity: 0.7"/>
+   </g>
+   <g id="patch_22">
+    <path d="M 791.354 215.513688 
+L 791.354 49.63425 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="patch_23">
+    <path d="M 968.881655 215.513688 
+L 968.881655 49.63425 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="patch_24">
+    <path d="M 791.354 215.513688 
+L 968.881655 215.513688 
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+   </g>
+   <g id="patch_25">
+    <path d="M 791.354 49.63425 
+L 968.881655 49.63425 
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+   </g>
+   <g id="text_67">
+    <g id="patch_26">
+     <path d="M 798.455106 72.517586 
+L 965.6887 72.517586 
+Q 967.9387 72.517586 967.9387 70.267586 
+L 967.9387 54.610633 
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+    </g>
+    <!-- empirical σ = 0.62 km/s -->
+    <g transform="translate(798.455106 60.309461) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-65"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(61.523438 0)"/>
+     <use xlink:href="#DejaVuSans-70" transform="translate(158.935547 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(222.412109 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(250.195312 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(291.308594 0)"/>
+     <use xlink:href="#DejaVuSans-63" transform="translate(319.091797 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(374.072266 0)"/>
+     <use xlink:href="#DejaVuSans-6c" transform="translate(435.351562 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(463.134766 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(494.921875 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(558.300781 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(590.087891 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(673.876953 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(705.664062 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(769.287109 0)"/>
+     <use xlink:href="#DejaVuSans-36" transform="translate(801.074219 0)"/>
+     <use xlink:href="#DejaVuSans-32" transform="translate(864.697266 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(928.320312 0)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(960.107422 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1018.017578 0)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(1115.429688 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(1149.121094 0)"/>
+    </g>
+    <!-- bias (mean − truth) = +0.02 km/s (+0.03 σ) -->
+    <g transform="translate(798.455106 68.707821) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-62"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(63.476562 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(91.259766 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(152.539062 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(204.638672 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(236.425781 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(275.439453 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(372.851562 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(434.375 0)"/>
+     <use xlink:href="#DejaVuSans-6e" transform="translate(495.654297 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(559.033203 0)"/>
+     <use xlink:href="#DejaVuSans-2212" transform="translate(590.820312 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(674.609375 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(706.396484 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(745.605469 0)"/>
+     <use xlink:href="#DejaVuSans-75" transform="translate(786.71875 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(850.097656 0)"/>
+     <use xlink:href="#DejaVuSans-68" transform="translate(889.306641 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(952.685547 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(991.699219 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(1023.486328 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1107.275391 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1139.0625 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1222.851562 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(1286.474609 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1318.261719 0)"/>
+     <use xlink:href="#DejaVuSans-32" transform="translate(1381.884766 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1445.507812 0)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(1477.294922 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1535.205078 0)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(1632.617188 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(1666.308594 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1718.408203 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(1750.195312 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1789.208984 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1872.998047 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(1936.621094 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1968.408203 0)"/>
+     <use xlink:href="#DejaVuSans-33" transform="translate(2032.03125 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(2095.654297 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(2127.441406 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(2190.820312 0)"/>
+    </g>
+   </g>
+   <g id="text_68">
+    <!-- $v_T$ (km/s) -->
+    <g transform="translate(857.317828 43.63425) scale(0.1 -0.1)">
+     <use xlink:href="#DejaVuSans-Oblique-76" transform="translate(0 0.015625)"/>
+     <use xlink:href="#DejaVuSans-Oblique-54" transform="translate(59.179688 -16.390625) scale(0.7)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(104.672852 0.015625)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(136.459961 0.015625)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(175.473633 0.015625)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(233.383789 0.015625)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(330.795898 0.015625)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(364.487305 0.015625)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(416.586914 0.015625)"/>
+    </g>
+   </g>
+  </g>
+  <g id="axes_5">
+   <g id="patch_27">
+    <path d="M 1027.082 215.513688 
+L 1204.609655 215.513688 
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+   </g>
+   <image xlink:href="data:image/png;base64,
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+   <g id="matplotlib.axis_9">
+    <g id="xtick_21">
+     <g id="line2d_108">
+      <path d="M 1067.241542 215.513688 
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+     </g>
+     <g id="line2d_109">
+      <g>
+       <use xlink:href="#m8e28d85ed1" x="1067.241542" y="215.513688" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_69">
+      <!-- −4 -->
+      <g transform="translate(1061.344667 228.592438) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-2212"/>
+       <use xlink:href="#DejaVuSans-34" transform="translate(83.789062 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_22">
+     <g id="line2d_110">
+      <path d="M 1111.93622 215.513688 
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+      <g>
+       <use xlink:href="#m8e28d85ed1" x="1111.93622" y="215.513688" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_70">
+      <!-- −3 -->
+      <g transform="translate(1106.039345 228.592438) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-2212"/>
+       <use xlink:href="#DejaVuSans-33" transform="translate(83.789062 0)"/>
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+    </g>
+    <g id="xtick_23">
+     <g id="line2d_112">
+      <path d="M 1156.630897 215.513688 
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+     </g>
+     <g id="line2d_113">
+      <g>
+       <use xlink:href="#m8e28d85ed1" x="1156.630897" y="215.513688" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_71">
+      <!-- −2 -->
+      <g transform="translate(1150.734022 228.592438) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-2212"/>
+       <use xlink:href="#DejaVuSans-32" transform="translate(83.789062 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_24">
+     <g id="line2d_114">
+      <path d="M 1201.325575 215.513688 
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+     </g>
+     <g id="line2d_115">
+      <g>
+       <use xlink:href="#m8e28d85ed1" x="1201.325575" y="215.513688" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_72">
+      <!-- −1 -->
+      <g transform="translate(1195.4287 228.592438) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-2212"/>
+       <use xlink:href="#DejaVuSans-31" transform="translate(83.789062 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_73">
+     <!-- Fitted $v_N$ (km/s) -->
+     <g transform="translate(1080.475828 241.094781) scale(0.09 -0.09)">
+      <defs>
+       <path id="DejaVuSans-Oblique-4e" d="M 1081 4666 
+L 1931 4666 
+L 3219 666 
+L 4000 4666 
+L 4616 4666 
+L 3706 0 
+L 2853 0 
+L 1569 4025 
+L 788 0 
+L 172 0 
+L 1081 4666 
+z
+" transform="scale(0.015625)"/>
+      </defs>
+      <use xlink:href="#DejaVuSans-46" transform="translate(0 0.015625)"/>
+      <use xlink:href="#DejaVuSans-69" transform="translate(57.519531 0.015625)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(85.302734 0.015625)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(124.511719 0.015625)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(163.720703 0.015625)"/>
+      <use xlink:href="#DejaVuSans-64" transform="translate(225.244141 0.015625)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(288.720703 0.015625)"/>
+      <use xlink:href="#DejaVuSans-Oblique-76" transform="translate(320.507812 0.015625)"/>
+      <use xlink:href="#DejaVuSans-Oblique-4e" transform="translate(379.6875 -16.390625) scale(0.7)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(434.785156 0.015625)"/>
+      <use xlink:href="#DejaVuSans-28" transform="translate(466.572266 0.015625)"/>
+      <use xlink:href="#DejaVuSans-6b" transform="translate(505.585938 0.015625)"/>
+      <use xlink:href="#DejaVuSans-6d" transform="translate(563.496094 0.015625)"/>
+      <use xlink:href="#DejaVuSans-2f" transform="translate(660.908203 0.015625)"/>
+      <use xlink:href="#DejaVuSans-73" transform="translate(694.599609 0.015625)"/>
+      <use xlink:href="#DejaVuSans-29" transform="translate(746.699219 0.015625)"/>
+     </g>
+    </g>
+   </g>
+   <g id="matplotlib.axis_10">
+    <g id="ytick_32">
+     <g id="line2d_116">
+      <path d="M 1027.082 215.513688 
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+     </g>
+     <g id="line2d_117">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="215.513688" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_74">
+      <!-- 0 -->
+      <g transform="translate(1014.992 218.553063) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_33">
+     <g id="line2d_118">
+      <path d="M 1027.082 195.516166 
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+     </g>
+     <g id="line2d_119">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="195.516166" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_75">
+      <!-- 10 -->
+      <g transform="translate(1009.902 198.555541) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_34">
+     <g id="line2d_120">
+      <path d="M 1027.082 175.518645 
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+     </g>
+     <g id="line2d_121">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="175.518645" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_76">
+      <!-- 20 -->
+      <g transform="translate(1009.902 178.55802) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_35">
+     <g id="line2d_122">
+      <path d="M 1027.082 155.521124 
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+     </g>
+     <g id="line2d_123">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="155.521124" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_77">
+      <!-- 30 -->
+      <g transform="translate(1009.902 158.560499) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-33"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_36">
+     <g id="line2d_124">
+      <path d="M 1027.082 135.523603 
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+     </g>
+     <g id="line2d_125">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="135.523603" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_78">
+      <!-- 40 -->
+      <g transform="translate(1009.902 138.562978) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_37">
+     <g id="line2d_126">
+      <path d="M 1027.082 115.526082 
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+     </g>
+     <g id="line2d_127">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="115.526082" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_79">
+      <!-- 50 -->
+      <g transform="translate(1009.902 118.565457) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_38">
+     <g id="line2d_128">
+      <path d="M 1027.082 95.528561 
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+     </g>
+     <g id="line2d_129">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="95.528561" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_80">
+      <!-- 60 -->
+      <g transform="translate(1009.902 98.567936) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-36"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_39">
+     <g id="line2d_130">
+      <path d="M 1027.082 75.53104 
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+     </g>
+     <g id="line2d_131">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="75.53104" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_81">
+      <!-- 70 -->
+      <g transform="translate(1009.902 78.570415) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-37"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_40">
+     <g id="line2d_132">
+      <path d="M 1027.082 55.533519 
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+     </g>
+     <g id="line2d_133">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="55.533519" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_82">
+      <!-- 80 -->
+      <g transform="translate(1009.902 58.572894) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-38"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_83">
+     <!-- MC count -->
+     <g transform="translate(1004.030281 153.724672) rotate(-90) scale(0.09 -0.09)">
+      <use xlink:href="#DejaVuSans-4d"/>
+      <use xlink:href="#DejaVuSans-43" transform="translate(86.279297 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(156.103516 0)"/>
+      <use xlink:href="#DejaVuSans-63" transform="translate(187.890625 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(242.871094 0)"/>
+      <use xlink:href="#DejaVuSans-75" transform="translate(304.052734 0)"/>
+      <use xlink:href="#DejaVuSans-6e" transform="translate(367.431641 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(430.810547 0)"/>
+     </g>
+    </g>
+   </g>
+   <g id="line2d_134">
+    <path d="M 1111.93622 215.513688 
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+   </g>
+   <g id="patch_28">
+    <path d="M 1027.082 215.513688 
+L 1027.082 49.63425 
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+   </g>
+   <g id="patch_29">
+    <path d="M 1204.609655 215.513688 
+L 1204.609655 49.63425 
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+   </g>
+   <g id="patch_30">
+    <path d="M 1027.082 215.513688 
+L 1204.609655 215.513688 
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+   </g>
+   <g id="patch_31">
+    <path d="M 1027.082 49.63425 
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+   </g>
+   <g id="text_84">
+    <g id="patch_32">
+     <path d="M 1034.183106 72.517586 
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+Q 1203.6667 72.517586 1203.6667 70.267586 
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+Q 1203.6667 52.360633 1201.4167 52.360633 
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+    </g>
+    <!-- empirical σ = 0.52 km/s -->
+    <g transform="translate(1034.183106 60.309461) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-65"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(61.523438 0)"/>
+     <use xlink:href="#DejaVuSans-70" transform="translate(158.935547 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(222.412109 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(250.195312 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(291.308594 0)"/>
+     <use xlink:href="#DejaVuSans-63" transform="translate(319.091797 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(374.072266 0)"/>
+     <use xlink:href="#DejaVuSans-6c" transform="translate(435.351562 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(463.134766 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(494.921875 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(558.300781 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(590.087891 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(673.876953 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(705.664062 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(769.287109 0)"/>
+     <use xlink:href="#DejaVuSans-35" transform="translate(801.074219 0)"/>
+     <use xlink:href="#DejaVuSans-32" transform="translate(864.697266 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(928.320312 0)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(960.107422 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1018.017578 0)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(1115.429688 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(1149.121094 0)"/>
+    </g>
+    <!-- bias (mean − truth) = +0.01 km/s (+0.01 σ) -->
+    <g transform="translate(1034.183106 68.707821) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-62"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(63.476562 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(91.259766 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(152.539062 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(204.638672 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(236.425781 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(275.439453 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(372.851562 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(434.375 0)"/>
+     <use xlink:href="#DejaVuSans-6e" transform="translate(495.654297 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(559.033203 0)"/>
+     <use xlink:href="#DejaVuSans-2212" transform="translate(590.820312 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(674.609375 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(706.396484 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(745.605469 0)"/>
+     <use xlink:href="#DejaVuSans-75" transform="translate(786.71875 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(850.097656 0)"/>
+     <use xlink:href="#DejaVuSans-68" transform="translate(889.306641 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(952.685547 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(991.699219 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(1023.486328 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1107.275391 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1139.0625 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1222.851562 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(1286.474609 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1318.261719 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(1381.884766 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1445.507812 0)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(1477.294922 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1535.205078 0)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(1632.617188 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(1666.308594 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1718.408203 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(1750.195312 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1789.208984 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1872.998047 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(1936.621094 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1968.408203 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(2032.03125 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(2095.654297 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(2127.441406 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(2190.820312 0)"/>
+    </g>
+   </g>
+   <g id="text_85">
+    <!-- $v_N$ (km/s) -->
+    <g transform="translate(1092.545828 43.63425) scale(0.1 -0.1)">
+     <use xlink:href="#DejaVuSans-Oblique-76" transform="translate(0 0.015625)"/>
+     <use xlink:href="#DejaVuSans-Oblique-4e" transform="translate(59.179688 -16.390625) scale(0.7)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(114.277344 0.015625)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(146.064453 0.015625)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(185.078125 0.015625)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(242.988281 0.015625)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(340.400391 0.015625)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(374.091797 0.015625)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(426.191406 0.015625)"/>
+    </g>
+   </g>
+  </g>
+  <g id="axes_6">
+   <g id="patch_33">
+    <path d="M 84.17 438.065125 
+L 261.697655 438.065125 
+L 261.697655 272.185687 
+L 84.17 272.185687 
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+      <g transform="translate(66.99 419.284) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-35" transform="translate(63.623047 0)"/>
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+      <!-- 50 -->
+      <g transform="translate(66.99 397.463501) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-35"/>
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+      <!-- 75 -->
+      <g transform="translate(66.99 375.643001) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-37"/>
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+     <g id="text_96">
+      <!-- 100 -->
+      <g transform="translate(61.9 353.822502) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-31"/>
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+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
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+      <!-- 125 -->
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+      <!-- 150 -->
+      <g transform="translate(61.9 310.181503) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-31"/>
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+      <g>
+       <use xlink:href="#m8e28d85ed1" x="135.844344" y="660.616562" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_164">
+      <!-- 4.95 -->
+      <g transform="translate(126.938094 673.695312) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-2e" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-39" transform="translate(95.410156 0)"/>
+       <use xlink:href="#DejaVuSans-35" transform="translate(159.033203 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_50">
+     <g id="line2d_263">
+      <path d="M 162.79016 660.616562 
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+     <g id="line2d_264">
+      <g>
+       <use xlink:href="#m8e28d85ed1" x="162.79016" y="660.616562" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_165">
+      <!-- 5.00 -->
+      <g transform="translate(153.88391 673.695312) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-2e" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(95.410156 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(159.033203 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_51">
+     <g id="line2d_265">
+      <path d="M 189.735977 660.616562 
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+     <g id="line2d_266">
+      <g>
+       <use xlink:href="#m8e28d85ed1" x="189.735977" y="660.616562" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_166">
+      <!-- 5.05 -->
+      <g transform="translate(180.829727 673.695312) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-2e" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(95.410156 0)"/>
+       <use xlink:href="#DejaVuSans-35" transform="translate(159.033203 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_52">
+     <g id="line2d_267">
+      <path d="M 216.681793 660.616562 
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+     <g id="line2d_268">
+      <g>
+       <use xlink:href="#m8e28d85ed1" x="216.681793" y="660.616562" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_167">
+      <!-- 5.10 -->
+      <g transform="translate(207.775543 673.695312) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-2e" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-31" transform="translate(95.410156 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(159.033203 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_53">
+     <g id="line2d_269">
+      <path d="M 243.62761 660.616562 
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+     </g>
+     <g id="line2d_270">
+      <g>
+       <use xlink:href="#m8e28d85ed1" x="243.62761" y="660.616562" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_168">
+      <!-- 5.15 -->
+      <g transform="translate(234.72136 673.695312) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-2e" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-31" transform="translate(95.410156 0)"/>
+       <use xlink:href="#DejaVuSans-35" transform="translate(159.033203 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_169">
+     <!-- Fitted Density (cm⁻³) -->
+     <g transform="translate(125.751328 686.197656) scale(0.09 -0.09)">
+      <use xlink:href="#DejaVuSans-46"/>
+      <use xlink:href="#DejaVuSans-69" transform="translate(50.269531 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(78.052734 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(117.261719 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(156.470703 0)"/>
+      <use xlink:href="#DejaVuSans-64" transform="translate(217.994141 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(281.470703 0)"/>
+      <use xlink:href="#DejaVuSans-44" transform="translate(313.257812 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(390.259766 0)"/>
+      <use xlink:href="#DejaVuSans-6e" transform="translate(451.783203 0)"/>
+      <use xlink:href="#DejaVuSans-73" transform="translate(515.162109 0)"/>
+      <use xlink:href="#DejaVuSans-69" transform="translate(567.261719 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(595.044922 0)"/>
+      <use xlink:href="#DejaVuSans-79" transform="translate(634.253906 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(693.433594 0)"/>
+      <use xlink:href="#DejaVuSans-28" transform="translate(725.220703 0)"/>
+      <use xlink:href="#DejaVuSans-63" transform="translate(764.234375 0)"/>
+      <use xlink:href="#DejaVuSans-6d" transform="translate(819.214844 0)"/>
+      <use xlink:href="#DejaVuSans-207b" transform="translate(916.626953 0)"/>
+      <use xlink:href="#DejaVuSans-b3" transform="translate(969.410156 0)"/>
+      <use xlink:href="#DejaVuSans-29" transform="translate(1009.498047 0)"/>
+     </g>
+    </g>
+   </g>
+   <g id="matplotlib.axis_22">
+    <g id="ytick_77">
+     <g id="line2d_271">
+      <path d="M 84.17 660.616562 
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+     </g>
+     <g id="line2d_272">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="84.17" y="660.616562" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_170">
+      <!-- 0 -->
+      <g transform="translate(72.08 663.655937) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_78">
+     <g id="line2d_273">
+      <path d="M 84.17 637.384148 
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+      <g>
+       <use xlink:href="#m75fa9cbc46" x="84.17" y="637.384148" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_171">
+      <!-- 10 -->
+      <g transform="translate(66.99 640.423523) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_79">
+     <g id="line2d_275">
+      <path d="M 84.17 614.151734 
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+     </g>
+     <g id="line2d_276">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="84.17" y="614.151734" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_172">
+      <!-- 20 -->
+      <g transform="translate(66.99 617.191109) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_80">
+     <g id="line2d_277">
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+     </g>
+     <g id="line2d_278">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="84.17" y="590.91932" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_173">
+      <!-- 30 -->
+      <g transform="translate(66.99 593.958695) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-33"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_81">
+     <g id="line2d_279">
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+     </g>
+     <g id="line2d_280">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="84.17" y="567.686906" style="stroke: #000000; stroke-width: 0.8"/>
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+     </g>
+     <g id="text_174">
+      <!-- 40 -->
+      <g transform="translate(66.99 570.726281) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_82">
+     <g id="line2d_281">
+      <path d="M 84.17 544.454491 
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+     </g>
+     <g id="line2d_282">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="84.17" y="544.454491" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_175">
+      <!-- 50 -->
+      <g transform="translate(66.99 547.493866) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_83">
+     <g id="line2d_283">
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+     </g>
+     <g id="line2d_284">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="84.17" y="521.222077" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_176">
+      <!-- 60 -->
+      <g transform="translate(66.99 524.261452) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-36"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_84">
+     <g id="line2d_285">
+      <path d="M 84.17 497.989663 
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+     </g>
+     <g id="line2d_286">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="84.17" y="497.989663" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_177">
+      <!-- 70 -->
+      <g transform="translate(66.99 501.029038) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-37"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_178">
+     <!-- MC count -->
+     <g transform="translate(61.118281 598.827547) rotate(-90) scale(0.09 -0.09)">
+      <use xlink:href="#DejaVuSans-4d"/>
+      <use xlink:href="#DejaVuSans-43" transform="translate(86.279297 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(156.103516 0)"/>
+      <use xlink:href="#DejaVuSans-63" transform="translate(187.890625 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(242.871094 0)"/>
+      <use xlink:href="#DejaVuSans-75" transform="translate(304.052734 0)"/>
+      <use xlink:href="#DejaVuSans-6e" transform="translate(367.431641 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(430.810547 0)"/>
+     </g>
+    </g>
+   </g>
+   <g id="line2d_287">
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+   </g>
+   <g id="patch_67">
+    <path d="M 84.17 660.616562 
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+   <g id="patch_68">
+    <path d="M 261.697655 660.616562 
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+   <g id="patch_69">
+    <path d="M 84.17 660.616562 
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+   <g id="patch_70">
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+   <g id="text_179">
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+    </g>
+    <!-- empirical σ = 0.047 cm⁻³ -->
+    <g transform="translate(91.271106 505.412336) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-65"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(61.523438 0)"/>
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+     <use xlink:href="#DejaVuSans-72" transform="translate(250.195312 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(291.308594 0)"/>
+     <use xlink:href="#DejaVuSans-63" transform="translate(319.091797 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(374.072266 0)"/>
+     <use xlink:href="#DejaVuSans-6c" transform="translate(435.351562 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(463.134766 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(494.921875 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(558.300781 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(590.087891 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(673.876953 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(705.664062 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(769.287109 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(801.074219 0)"/>
+     <use xlink:href="#DejaVuSans-34" transform="translate(864.697266 0)"/>
+     <use xlink:href="#DejaVuSans-37" transform="translate(928.320312 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(991.943359 0)"/>
+     <use xlink:href="#DejaVuSans-63" transform="translate(1023.730469 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1078.710938 0)"/>
+     <use xlink:href="#DejaVuSans-207b" transform="translate(1176.123047 0)"/>
+     <use xlink:href="#DejaVuSans-b3" transform="translate(1228.90625 0)"/>
+    </g>
+    <!-- bias (mean − truth) = +0.010 cm⁻³ (+0.22 σ) -->
+    <g transform="translate(91.271106 513.810696) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-62"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(63.476562 0)"/>
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+     <use xlink:href="#DejaVuSans-6d" transform="translate(275.439453 0)"/>
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+     <use xlink:href="#DejaVuSans-61" transform="translate(434.375 0)"/>
+     <use xlink:href="#DejaVuSans-6e" transform="translate(495.654297 0)"/>
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+     <use xlink:href="#DejaVuSans-2212" transform="translate(590.820312 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(674.609375 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(706.396484 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(745.605469 0)"/>
+     <use xlink:href="#DejaVuSans-75" transform="translate(786.71875 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(850.097656 0)"/>
+     <use xlink:href="#DejaVuSans-68" transform="translate(889.306641 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(952.685547 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(991.699219 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(1023.486328 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1107.275391 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1139.0625 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1222.851562 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(1286.474609 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1318.261719 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(1381.884766 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1445.507812 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1509.130859 0)"/>
+     <use xlink:href="#DejaVuSans-63" transform="translate(1540.917969 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1595.898438 0)"/>
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+       <use xlink:href="#DejaVuSans-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_86">
+     <g id="line2d_299">
+      <path d="M 319.898 637.72085 
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+     </g>
+     <g id="line2d_300">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="319.898" y="637.72085" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_188">
+      <!-- 10 -->
+      <g transform="translate(302.718 640.760225) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_87">
+     <g id="line2d_301">
+      <path d="M 319.898 614.825137 
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+     </g>
+     <g id="line2d_302">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="319.898" y="614.825137" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_189">
+      <!-- 20 -->
+      <g transform="translate(302.718 617.864512) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_88">
+     <g id="line2d_303">
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+     </g>
+     <g id="line2d_304">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="319.898" y="591.929425" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_190">
+      <!-- 30 -->
+      <g transform="translate(302.718 594.9688) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-33"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_89">
+     <g id="line2d_305">
+      <path d="M 319.898 569.033712 
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+     </g>
+     <g id="line2d_306">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="319.898" y="569.033712" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_191">
+      <!-- 40 -->
+      <g transform="translate(302.718 572.073087) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_90">
+     <g id="line2d_307">
+      <path d="M 319.898 546.138 
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+     </g>
+     <g id="line2d_308">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="319.898" y="546.138" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_192">
+      <!-- 50 -->
+      <g transform="translate(302.718 549.177375) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_91">
+     <g id="line2d_309">
+      <path d="M 319.898 523.242287 
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+     </g>
+     <g id="line2d_310">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="319.898" y="523.242287" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_193">
+      <!-- 60 -->
+      <g transform="translate(302.718 526.281662) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-36"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_92">
+     <g id="line2d_311">
+      <path d="M 319.898 500.346575 
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+     </g>
+     <g id="line2d_312">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="319.898" y="500.346575" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_194">
+      <!-- 70 -->
+      <g transform="translate(302.718 503.38595) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-37"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_195">
+     <!-- MC count -->
+     <g transform="translate(296.846281 598.827547) rotate(-90) scale(0.09 -0.09)">
+      <use xlink:href="#DejaVuSans-4d"/>
+      <use xlink:href="#DejaVuSans-43" transform="translate(86.279297 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(156.103516 0)"/>
+      <use xlink:href="#DejaVuSans-63" transform="translate(187.890625 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(242.871094 0)"/>
+      <use xlink:href="#DejaVuSans-75" transform="translate(304.052734 0)"/>
+      <use xlink:href="#DejaVuSans-6e" transform="translate(367.431641 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(430.810547 0)"/>
+     </g>
+    </g>
+   </g>
+   <g id="line2d_313">
+    <path d="M 406.511422 660.616562 
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+   </g>
+   <g id="patch_74">
+    <path d="M 319.898 660.616562 
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+   </g>
+   <g id="patch_75">
+    <path d="M 497.425655 660.616562 
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+   </g>
+   <g id="patch_76">
+    <path d="M 319.898 660.616562 
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+   </g>
+   <g id="patch_77">
+    <path d="M 319.898 494.737125 
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+   </g>
+   <g id="text_196">
+    <g id="patch_78">
+     <path d="M 326.999106 517.620461 
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+    </g>
+    <!-- empirical σ = 1.22e+03 K -->
+    <g transform="translate(326.999106 505.412336) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-65"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(61.523438 0)"/>
+     <use xlink:href="#DejaVuSans-70" transform="translate(158.935547 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(222.412109 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(250.195312 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(291.308594 0)"/>
+     <use xlink:href="#DejaVuSans-63" transform="translate(319.091797 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(374.072266 0)"/>
+     <use xlink:href="#DejaVuSans-6c" transform="translate(435.351562 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(463.134766 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(494.921875 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(558.300781 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(590.087891 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(673.876953 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(705.664062 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(769.287109 0)"/>
+     <use xlink:href="#DejaVuSans-32" transform="translate(801.074219 0)"/>
+     <use xlink:href="#DejaVuSans-32" transform="translate(864.697266 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(928.320312 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(989.84375 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1073.632812 0)"/>
+     <use xlink:href="#DejaVuSans-33" transform="translate(1137.255859 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1200.878906 0)"/>
+     <use xlink:href="#DejaVuSans-4b" transform="translate(1232.666016 0)"/>
+    </g>
+    <!-- bias (mean − truth) = +1.76e+02 K (+0.14 σ) -->
+    <g transform="translate(326.999106 513.810696) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-62"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(63.476562 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(91.259766 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(152.539062 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(204.638672 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(236.425781 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(275.439453 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(372.851562 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(434.375 0)"/>
+     <use xlink:href="#DejaVuSans-6e" transform="translate(495.654297 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(559.033203 0)"/>
+     <use xlink:href="#DejaVuSans-2212" transform="translate(590.820312 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(674.609375 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(706.396484 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(745.605469 0)"/>
+     <use xlink:href="#DejaVuSans-75" transform="translate(786.71875 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(850.097656 0)"/>
+     <use xlink:href="#DejaVuSans-68" transform="translate(889.306641 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(952.685547 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(991.699219 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(1023.486328 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1107.275391 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1139.0625 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(1222.851562 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(1286.474609 0)"/>
+     <use xlink:href="#DejaVuSans-37" transform="translate(1318.261719 0)"/>
+     <use xlink:href="#DejaVuSans-36" transform="translate(1381.884766 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(1445.507812 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1507.03125 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1590.820312 0)"/>
+     <use xlink:href="#DejaVuSans-32" transform="translate(1654.443359 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1718.066406 0)"/>
+     <use xlink:href="#DejaVuSans-4b" transform="translate(1749.853516 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1815.429688 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(1847.216797 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1886.230469 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1970.019531 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(2033.642578 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(2065.429688 0)"/>
+     <use xlink:href="#DejaVuSans-34" transform="translate(2129.052734 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(2192.675781 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(2224.462891 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(2287.841797 0)"/>
+    </g>
+   </g>
+   <g id="text_197">
+    <!-- Temperature (K) -->
+    <g transform="translate(368.222765 488.737125) scale(0.1 -0.1)">
+     <use xlink:href="#DejaVuSans-54"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(44.083984 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(105.607422 0)"/>
+     <use xlink:href="#DejaVuSans-70" transform="translate(203.019531 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(266.496094 0)"/>
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+     <use xlink:href="#DejaVuSans-61" transform="translate(369.132812 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(430.412109 0)"/>
+     <use xlink:href="#DejaVuSans-75" transform="translate(469.621094 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(533 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(571.863281 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(633.386719 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(665.173828 0)"/>
+     <use xlink:href="#DejaVuSans-4b" transform="translate(704.1875 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(769.763672 0)"/>
+    </g>
+   </g>
+  </g>
+  <g id="axes_13">
+   <g id="patch_79">
+    <path d="M 555.626 660.616562 
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+     <g id="line2d_334">
+      <path d="M 555.626 570.342039 
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+" clip-path="url(#p652e4eb155)" style="fill: none; stroke: #b0b0b0; stroke-opacity: 0.25; stroke-width: 0.8; stroke-linecap: square"/>
+     </g>
+     <g id="line2d_335">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="555.626" y="570.342039" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_209">
+      <!-- 40 -->
+      <g transform="translate(538.446 573.381414) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_98">
+     <g id="line2d_336">
+      <path d="M 555.626 547.773408 
+L 733.153655 547.773408 
+" clip-path="url(#p652e4eb155)" style="fill: none; stroke: #b0b0b0; stroke-opacity: 0.25; stroke-width: 0.8; stroke-linecap: square"/>
+     </g>
+     <g id="line2d_337">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="555.626" y="547.773408" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_210">
+      <!-- 50 -->
+      <g transform="translate(538.446 550.812783) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_99">
+     <g id="line2d_338">
+      <path d="M 555.626 525.204777 
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+     </g>
+     <g id="line2d_339">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="555.626" y="525.204777" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_211">
+      <!-- 60 -->
+      <g transform="translate(538.446 528.244152) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-36"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_100">
+     <g id="line2d_340">
+      <path d="M 555.626 502.636146 
+L 733.153655 502.636146 
+" clip-path="url(#p652e4eb155)" style="fill: none; stroke: #b0b0b0; stroke-opacity: 0.25; stroke-width: 0.8; stroke-linecap: square"/>
+     </g>
+     <g id="line2d_341">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="555.626" y="502.636146" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_212">
+      <!-- 70 -->
+      <g transform="translate(538.446 505.675521) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-37"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_213">
+     <!-- MC count -->
+     <g transform="translate(532.574281 598.827547) rotate(-90) scale(0.09 -0.09)">
+      <use xlink:href="#DejaVuSans-4d"/>
+      <use xlink:href="#DejaVuSans-43" transform="translate(86.279297 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(156.103516 0)"/>
+      <use xlink:href="#DejaVuSans-63" transform="translate(187.890625 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(242.871094 0)"/>
+      <use xlink:href="#DejaVuSans-75" transform="translate(304.052734 0)"/>
+      <use xlink:href="#DejaVuSans-6e" transform="translate(367.431641 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(430.810547 0)"/>
+     </g>
+    </g>
+   </g>
+   <g id="line2d_342">
+    <path d="M 647.46229 660.616562 
+L 647.46229 494.737125 
+" clip-path="url(#p652e4eb155)" style="fill: none; stroke-dasharray: 3.7,1.6; stroke-dashoffset: 0; stroke: #000000; stroke-opacity: 0.7"/>
+   </g>
+   <g id="patch_80">
+    <path d="M 555.626 660.616562 
+L 555.626 494.737125 
+" style="fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square"/>
+   </g>
+   <g id="patch_81">
+    <path d="M 733.153655 660.616562 
+L 733.153655 494.737125 
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+   </g>
+   <g id="patch_82">
+    <path d="M 555.626 660.616562 
+L 733.153655 660.616562 
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+   </g>
+   <g id="patch_83">
+    <path d="M 555.626 494.737125 
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+   </g>
+   <g id="text_214">
+    <g id="patch_84">
+     <path d="M 562.727106 517.620461 
+L 722.802887 517.620461 
+Q 725.052887 517.620461 725.052887 515.370461 
+L 725.052887 499.713508 
+Q 725.052887 497.463508 722.802887 497.463508 
+L 562.727106 497.463508 
+Q 560.477106 497.463508 560.477106 499.713508 
+L 560.477106 515.370461 
+Q 560.477106 517.620461 562.727106 517.620461 
+z
+" style="fill: #ffffff; opacity: 0.8; stroke: #000000; stroke-linejoin: miter"/>
+    </g>
+    <!-- empirical σ = 0.20 km/s -->
+    <g transform="translate(562.727106 505.412336) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-65"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(61.523438 0)"/>
+     <use xlink:href="#DejaVuSans-70" transform="translate(158.935547 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(222.412109 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(250.195312 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(291.308594 0)"/>
+     <use xlink:href="#DejaVuSans-63" transform="translate(319.091797 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(374.072266 0)"/>
+     <use xlink:href="#DejaVuSans-6c" transform="translate(435.351562 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(463.134766 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(494.921875 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(558.300781 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(590.087891 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(673.876953 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(705.664062 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(769.287109 0)"/>
+     <use xlink:href="#DejaVuSans-32" transform="translate(801.074219 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(864.697266 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(928.320312 0)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(960.107422 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1018.017578 0)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(1115.429688 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(1149.121094 0)"/>
+    </g>
+    <!-- bias (mean − truth) = -0.00 km/s (-0.02 σ) -->
+    <g transform="translate(562.727106 513.810696) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-62"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(63.476562 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(91.259766 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(152.539062 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(204.638672 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(236.425781 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(275.439453 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(372.851562 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(434.375 0)"/>
+     <use xlink:href="#DejaVuSans-6e" transform="translate(495.654297 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(559.033203 0)"/>
+     <use xlink:href="#DejaVuSans-2212" transform="translate(590.820312 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(674.609375 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(706.396484 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(745.605469 0)"/>
+     <use xlink:href="#DejaVuSans-75" transform="translate(786.71875 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(850.097656 0)"/>
+     <use xlink:href="#DejaVuSans-68" transform="translate(889.306641 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(952.685547 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(991.699219 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(1023.486328 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1107.275391 0)"/>
+     <use xlink:href="#DejaVuSans-2d" transform="translate(1139.0625 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1175.146484 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(1238.769531 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1270.556641 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1334.179688 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1397.802734 0)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(1429.589844 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1487.5 0)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(1584.912109 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(1618.603516 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1670.703125 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(1702.490234 0)"/>
+     <use xlink:href="#DejaVuSans-2d" transform="translate(1741.503906 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1777.587891 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(1841.210938 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1872.998047 0)"/>
+     <use xlink:href="#DejaVuSans-32" transform="translate(1936.621094 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(2000.244141 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(2032.03125 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(2095.410156 0)"/>
+    </g>
+   </g>
+   <g id="text_215">
+    <!-- $v_R$ (km/s) -->
+    <g transform="translate(621.289828 488.737125) scale(0.1 -0.1)">
+     <use xlink:href="#DejaVuSans-Oblique-76" transform="translate(0 0.015625)"/>
+     <use xlink:href="#DejaVuSans-Oblique-52" transform="translate(59.179688 -16.390625) scale(0.7)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(110.551758 0.015625)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(142.338867 0.015625)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(181.352539 0.015625)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(239.262695 0.015625)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(336.674805 0.015625)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(370.366211 0.015625)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(422.46582 0.015625)"/>
+    </g>
+   </g>
+  </g>
+  <g id="axes_14">
+   <g id="patch_85">
+    <path d="M 791.354 660.616562 
+L 968.881655 660.616562 
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+L 791.354 494.737125 
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+" style="fill: #ffffff"/>
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+      </g>
+     </g>
+    </g>
+    <g id="ytick_108">
+     <g id="line2d_367">
+      <path d="M 791.354 513.168174 
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+     </g>
+     <g id="line2d_368">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="791.354" y="513.168174" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_229">
+      <!-- 70 -->
+      <g transform="translate(774.174 516.207549) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-37"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_230">
+     <!-- MC count -->
+     <g transform="translate(768.302281 598.827547) rotate(-90) scale(0.09 -0.09)">
+      <use xlink:href="#DejaVuSans-4d"/>
+      <use xlink:href="#DejaVuSans-43" transform="translate(86.279297 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(156.103516 0)"/>
+      <use xlink:href="#DejaVuSans-63" transform="translate(187.890625 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(242.871094 0)"/>
+      <use xlink:href="#DejaVuSans-75" transform="translate(304.052734 0)"/>
+      <use xlink:href="#DejaVuSans-6e" transform="translate(367.431641 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(430.810547 0)"/>
+     </g>
+    </g>
+   </g>
+   <g id="line2d_369">
+    <path d="M 883.703652 660.616562 
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+   </g>
+   <g id="patch_86">
+    <path d="M 791.354 660.616562 
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+   </g>
+   <g id="patch_87">
+    <path d="M 968.881655 660.616562 
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+   </g>
+   <g id="patch_88">
+    <path d="M 791.354 660.616562 
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+   </g>
+   <g id="patch_89">
+    <path d="M 791.354 494.737125 
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+   </g>
+   <g id="text_231">
+    <g id="patch_90">
+     <path d="M 798.455106 517.620461 
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+    </g>
+    <!-- empirical σ = 0.66 km/s -->
+    <g transform="translate(798.455106 505.412336) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-65"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(61.523438 0)"/>
+     <use xlink:href="#DejaVuSans-70" transform="translate(158.935547 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(222.412109 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(250.195312 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(291.308594 0)"/>
+     <use xlink:href="#DejaVuSans-63" transform="translate(319.091797 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(374.072266 0)"/>
+     <use xlink:href="#DejaVuSans-6c" transform="translate(435.351562 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(463.134766 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(494.921875 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(558.300781 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(590.087891 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(673.876953 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(705.664062 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(769.287109 0)"/>
+     <use xlink:href="#DejaVuSans-36" transform="translate(801.074219 0)"/>
+     <use xlink:href="#DejaVuSans-36" transform="translate(864.697266 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(928.320312 0)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(960.107422 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1018.017578 0)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(1115.429688 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(1149.121094 0)"/>
+    </g>
+    <!-- bias (mean − truth) = +0.01 km/s (+0.02 σ) -->
+    <g transform="translate(798.455106 513.810696) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-62"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(63.476562 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(91.259766 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(152.539062 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(204.638672 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(236.425781 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(275.439453 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(372.851562 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(434.375 0)"/>
+     <use xlink:href="#DejaVuSans-6e" transform="translate(495.654297 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(559.033203 0)"/>
+     <use xlink:href="#DejaVuSans-2212" transform="translate(590.820312 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(674.609375 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(706.396484 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(745.605469 0)"/>
+     <use xlink:href="#DejaVuSans-75" transform="translate(786.71875 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(850.097656 0)"/>
+     <use xlink:href="#DejaVuSans-68" transform="translate(889.306641 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(952.685547 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(991.699219 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(1023.486328 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1107.275391 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1139.0625 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1222.851562 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(1286.474609 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1318.261719 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(1381.884766 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1445.507812 0)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(1477.294922 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1535.205078 0)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(1632.617188 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(1666.308594 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1718.408203 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(1750.195312 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1789.208984 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1872.998047 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(1936.621094 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1968.408203 0)"/>
+     <use xlink:href="#DejaVuSans-32" transform="translate(2032.03125 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(2095.654297 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(2127.441406 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(2190.820312 0)"/>
+    </g>
+   </g>
+   <g id="text_232">
+    <!-- $v_T$ (km/s) -->
+    <g transform="translate(857.317828 488.737125) scale(0.1 -0.1)">
+     <use xlink:href="#DejaVuSans-Oblique-76" transform="translate(0 0.015625)"/>
+     <use xlink:href="#DejaVuSans-Oblique-54" transform="translate(59.179688 -16.390625) scale(0.7)"/>
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+     <use xlink:href="#DejaVuSans-6b" transform="translate(175.473633 0.015625)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(233.383789 0.015625)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(330.795898 0.015625)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(364.487305 0.015625)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(416.586914 0.015625)"/>
+    </g>
+   </g>
+  </g>
+  <g id="axes_15">
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+      <!-- −4 -->
+      <g transform="translate(1065.151474 673.695312) scale(0.08 -0.08)">
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+" clip-path="url(#p7f5758b496)" style="fill: none; stroke: #b0b0b0; stroke-opacity: 0.25; stroke-width: 0.8; stroke-linecap: square"/>
+     </g>
+     <g id="line2d_373">
+      <g>
+       <use xlink:href="#m8e28d85ed1" x="1115.232629" y="660.616562" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_234">
+      <!-- −3 -->
+      <g transform="translate(1109.335754 673.695312) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-2212"/>
+       <use xlink:href="#DejaVuSans-33" transform="translate(83.789062 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_71">
+     <g id="line2d_374">
+      <path d="M 1159.416909 660.616562 
+L 1159.416909 494.737125 
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+     </g>
+     <g id="line2d_375">
+      <g>
+       <use xlink:href="#m8e28d85ed1" x="1159.416909" y="660.616562" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_235">
+      <!-- −2 -->
+      <g transform="translate(1153.520034 673.695312) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-2212"/>
+       <use xlink:href="#DejaVuSans-32" transform="translate(83.789062 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_72">
+     <g id="line2d_376">
+      <path d="M 1203.601189 660.616562 
+L 1203.601189 494.737125 
+" clip-path="url(#p7f5758b496)" style="fill: none; stroke: #b0b0b0; stroke-opacity: 0.25; stroke-width: 0.8; stroke-linecap: square"/>
+     </g>
+     <g id="line2d_377">
+      <g>
+       <use xlink:href="#m8e28d85ed1" x="1203.601189" y="660.616562" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_236">
+      <!-- −1 -->
+      <g transform="translate(1197.704314 673.695312) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-2212"/>
+       <use xlink:href="#DejaVuSans-31" transform="translate(83.789062 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_237">
+     <!-- Fitted $v_N$ (km/s) -->
+     <g transform="translate(1080.475828 686.197656) scale(0.09 -0.09)">
+      <use xlink:href="#DejaVuSans-46" transform="translate(0 0.015625)"/>
+      <use xlink:href="#DejaVuSans-69" transform="translate(57.519531 0.015625)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(85.302734 0.015625)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(124.511719 0.015625)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(163.720703 0.015625)"/>
+      <use xlink:href="#DejaVuSans-64" transform="translate(225.244141 0.015625)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(288.720703 0.015625)"/>
+      <use xlink:href="#DejaVuSans-Oblique-76" transform="translate(320.507812 0.015625)"/>
+      <use xlink:href="#DejaVuSans-Oblique-4e" transform="translate(379.6875 -16.390625) scale(0.7)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(434.785156 0.015625)"/>
+      <use xlink:href="#DejaVuSans-28" transform="translate(466.572266 0.015625)"/>
+      <use xlink:href="#DejaVuSans-6b" transform="translate(505.585938 0.015625)"/>
+      <use xlink:href="#DejaVuSans-6d" transform="translate(563.496094 0.015625)"/>
+      <use xlink:href="#DejaVuSans-2f" transform="translate(660.908203 0.015625)"/>
+      <use xlink:href="#DejaVuSans-73" transform="translate(694.599609 0.015625)"/>
+      <use xlink:href="#DejaVuSans-29" transform="translate(746.699219 0.015625)"/>
+     </g>
+    </g>
+   </g>
+   <g id="matplotlib.axis_30">
+    <g id="ytick_109">
+     <g id="line2d_378">
+      <path d="M 1027.082 660.616562 
+L 1204.609655 660.616562 
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+     </g>
+     <g id="line2d_379">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="660.616562" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_238">
+      <!-- 0 -->
+      <g transform="translate(1014.992 663.655937) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_110">
+     <g id="line2d_380">
+      <path d="M 1027.082 637.384148 
+L 1204.609655 637.384148 
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+     </g>
+     <g id="line2d_381">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="637.384148" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_239">
+      <!-- 10 -->
+      <g transform="translate(1009.902 640.423523) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_111">
+     <g id="line2d_382">
+      <path d="M 1027.082 614.151734 
+L 1204.609655 614.151734 
+" clip-path="url(#p7f5758b496)" style="fill: none; stroke: #b0b0b0; stroke-opacity: 0.25; stroke-width: 0.8; stroke-linecap: square"/>
+     </g>
+     <g id="line2d_383">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="614.151734" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_240">
+      <!-- 20 -->
+      <g transform="translate(1009.902 617.191109) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_112">
+     <g id="line2d_384">
+      <path d="M 1027.082 590.91932 
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+     </g>
+     <g id="line2d_385">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="590.91932" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_241">
+      <!-- 30 -->
+      <g transform="translate(1009.902 593.958695) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-33"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_113">
+     <g id="line2d_386">
+      <path d="M 1027.082 567.686906 
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+     </g>
+     <g id="line2d_387">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="567.686906" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_242">
+      <!-- 40 -->
+      <g transform="translate(1009.902 570.726281) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_114">
+     <g id="line2d_388">
+      <path d="M 1027.082 544.454491 
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+     </g>
+     <g id="line2d_389">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="544.454491" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_243">
+      <!-- 50 -->
+      <g transform="translate(1009.902 547.493866) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_115">
+     <g id="line2d_390">
+      <path d="M 1027.082 521.222077 
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+     </g>
+     <g id="line2d_391">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="521.222077" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_244">
+      <!-- 60 -->
+      <g transform="translate(1009.902 524.261452) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-36"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_116">
+     <g id="line2d_392">
+      <path d="M 1027.082 497.989663 
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+     </g>
+     <g id="line2d_393">
+      <g>
+       <use xlink:href="#m75fa9cbc46" x="1027.082" y="497.989663" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_245">
+      <!-- 70 -->
+      <g transform="translate(1009.902 501.029038) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-37"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_246">
+     <!-- MC count -->
+     <g transform="translate(1004.030281 598.827547) rotate(-90) scale(0.09 -0.09)">
+      <use xlink:href="#DejaVuSans-4d"/>
+      <use xlink:href="#DejaVuSans-43" transform="translate(86.279297 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(156.103516 0)"/>
+      <use xlink:href="#DejaVuSans-63" transform="translate(187.890625 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(242.871094 0)"/>
+      <use xlink:href="#DejaVuSans-75" transform="translate(304.052734 0)"/>
+      <use xlink:href="#DejaVuSans-6e" transform="translate(367.431641 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(430.810547 0)"/>
+     </g>
+    </g>
+   </g>
+   <g id="line2d_394">
+    <path d="M 1115.232629 660.616562 
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+   </g>
+   <g id="patch_92">
+    <path d="M 1027.082 660.616562 
+L 1027.082 494.737125 
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+   </g>
+   <g id="patch_93">
+    <path d="M 1204.609655 660.616562 
+L 1204.609655 494.737125 
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+   </g>
+   <g id="patch_94">
+    <path d="M 1027.082 660.616562 
+L 1204.609655 660.616562 
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+   </g>
+   <g id="patch_95">
+    <path d="M 1027.082 494.737125 
+L 1204.609655 494.737125 
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+   </g>
+   <g id="text_247">
+    <g id="patch_96">
+     <path d="M 1034.183106 517.620461 
+L 1201.4167 517.620461 
+Q 1203.6667 517.620461 1203.6667 515.370461 
+L 1203.6667 499.713508 
+Q 1203.6667 497.463508 1201.4167 497.463508 
+L 1034.183106 497.463508 
+Q 1031.933106 497.463508 1031.933106 499.713508 
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+Q 1031.933106 517.620461 1034.183106 517.620461 
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+" style="fill: #ffffff; opacity: 0.8; stroke: #000000; stroke-linejoin: miter"/>
+    </g>
+    <!-- empirical σ = 0.56 km/s -->
+    <g transform="translate(1034.183106 505.412336) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-65"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(61.523438 0)"/>
+     <use xlink:href="#DejaVuSans-70" transform="translate(158.935547 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(222.412109 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(250.195312 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(291.308594 0)"/>
+     <use xlink:href="#DejaVuSans-63" transform="translate(319.091797 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(374.072266 0)"/>
+     <use xlink:href="#DejaVuSans-6c" transform="translate(435.351562 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(463.134766 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(494.921875 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(558.300781 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(590.087891 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(673.876953 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(705.664062 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(769.287109 0)"/>
+     <use xlink:href="#DejaVuSans-35" transform="translate(801.074219 0)"/>
+     <use xlink:href="#DejaVuSans-36" transform="translate(864.697266 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(928.320312 0)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(960.107422 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1018.017578 0)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(1115.429688 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(1149.121094 0)"/>
+    </g>
+    <!-- bias (mean − truth) = +0.01 km/s (+0.01 σ) -->
+    <g transform="translate(1034.183106 513.810696) scale(0.075 -0.075)">
+     <use xlink:href="#DejaVuSans-62"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(63.476562 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(91.259766 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(152.539062 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(204.638672 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(236.425781 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(275.439453 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(372.851562 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(434.375 0)"/>
+     <use xlink:href="#DejaVuSans-6e" transform="translate(495.654297 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(559.033203 0)"/>
+     <use xlink:href="#DejaVuSans-2212" transform="translate(590.820312 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(674.609375 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(706.396484 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(745.605469 0)"/>
+     <use xlink:href="#DejaVuSans-75" transform="translate(786.71875 0)"/>
+     <use xlink:href="#DejaVuSans-74" transform="translate(850.097656 0)"/>
+     <use xlink:href="#DejaVuSans-68" transform="translate(889.306641 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(952.685547 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(991.699219 0)"/>
+     <use xlink:href="#DejaVuSans-3d" transform="translate(1023.486328 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1107.275391 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1139.0625 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1222.851562 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(1286.474609 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1318.261719 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(1381.884766 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1445.507812 0)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(1477.294922 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1535.205078 0)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(1632.617188 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(1666.308594 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1718.408203 0)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(1750.195312 0)"/>
+     <use xlink:href="#DejaVuSans-2b" transform="translate(1789.208984 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1872.998047 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(1936.621094 0)"/>
+     <use xlink:href="#DejaVuSans-30" transform="translate(1968.408203 0)"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(2032.03125 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(2095.654297 0)"/>
+     <use xlink:href="#DejaVuSans-3c3" transform="translate(2127.441406 0)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(2190.820312 0)"/>
+    </g>
+   </g>
+   <g id="text_248">
+    <!-- $v_N$ (km/s) -->
+    <g transform="translate(1092.545828 488.737125) scale(0.1 -0.1)">
+     <use xlink:href="#DejaVuSans-Oblique-76" transform="translate(0 0.015625)"/>
+     <use xlink:href="#DejaVuSans-Oblique-4e" transform="translate(59.179688 -16.390625) scale(0.7)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(114.277344 0.015625)"/>
+     <use xlink:href="#DejaVuSans-28" transform="translate(146.064453 0.015625)"/>
+     <use xlink:href="#DejaVuSans-6b" transform="translate(185.078125 0.015625)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(242.988281 0.015625)"/>
+     <use xlink:href="#DejaVuSans-2f" transform="translate(340.400391 0.015625)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(374.091797 0.015625)"/>
+     <use xlink:href="#DejaVuSans-29" transform="translate(426.191406 0.015625)"/>
+    </g>
+   </g>
+  </g>
+  <g id="axes_16">
+   <g id="patch_97">
+    <path d="M 84.17 883.168 
+L 261.697655 883.168 
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+      <!-- 50 -->
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+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
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+      <!-- 75 -->
+      <g transform="translate(66.99 822.16126) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-37"/>
+       <use xlink:href="#DejaVuSans-35" transform="translate(63.623047 0)"/>
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+     <g id="text_259">
+      <!-- 100 -->
+      <g transform="translate(61.9 800.812555) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
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+      <!-- 125 -->
+      <g transform="translate(61.9 779.46385) scale(0.08 -0.08)">
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+      <!-- 150 -->
+      <g transform="translate(61.9 758.115145) scale(0.08 -0.08)">
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+      <!-- 175 -->
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+      <use xlink:href="#DejaVuSans-6e" transform="translate(73.193359 0)"/>
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+     <g id="text_325">
+      <!-- 250 -->
+      <g transform="translate(1004.812 741.875597) scale(0.08 -0.08)">
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-35" transform="translate(63.623047 0)"/>
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diff --git a/docs/swapi/solar-wind-moments.md b/docs/swapi/solar-wind-moments.md
new file mode 100644
index 000000000..0712f405a
--- /dev/null
+++ b/docs/swapi/solar-wind-moments.md
@@ -0,0 +1,662 @@
+# SWAPI Solar Wind Moments Algorithm Notes
+
+## Introduction
+
+The SWAPI instrument on IMAP measures coincidence count rates as a function of the ESA voltage that selects ions at a given energy-per-charge. Each 12-second ESA sweep contains 72 voltage steps spanning ~50 V to ~10 kV (covering proton and alpha solar-wind energies, plus the pickup-ion shoulder). The L3A `proton-sw` pipeline groups 5 consecutive sweeps into a 60-second chunk and fits a Maxwellian proton VDF — yielding density $`n`$, temperature $`T`$, and the three components of the bulk velocity $`\mathbf{v}_{\text{RTN}}`$ — convolved through SWAPI's energy/angle response and the deadtime correction. This document describes that pipeline end-to-end: the inputs, the forward model, the optimization, and the per-fit uncertainty estimate reported in the L3A CDF.
+
+To set the stage, here is one representative chunk from `imap_swapi_l2_sci_20260201_v001.cdf` (sweeps 6853–6857, center 22:50:14 UT, slow ~308 km/s wind). The figure below shows the measured count rate at each of the 72 ESA steps for all 5 sweeps and on the 5-sweep average. Two forward-modeled spectra are overlaid: the production fit (orange — uses all 71 science bins, including the high-resolution fine-sweep block 63–71) and a coarse-only variant (blue — drops the fine sweep). Both forward models closely track the data on every sweep:
+
+<img src="figures/real_data_fit.svg" alt="SWAPI proton fit on real L2 data: 5 individual sweeps and the 5-sweep average, with the production fit (all 71 science bins) and a coarse-only fit (62 bins) overlaid on the measured count rate" width="1200">
+
+The fitted moments and their 1σ uncertainties are:
+
+<!-- BEGIN: real_data_table (auto-generated by docs/swapi/figure_src/plot_real_data_fit.py — do not edit by hand) -->
+| Quantity | SWAPI all bins (HC3 σ) | SWAPI all bins (boot σ) | SWAPI coarse only (HC3 σ) | WIND/SWE 2-min @ 22:49:48 UT |
+|---|---:|---:|---:|---:|
+| Density $`n`$ (cm⁻³) | 5.122 ± 0.094 | ± 0.099 | 5.091 ± 0.134 | 5.710 ± 0.053 |
+| Temperature $`T`$ (K) | $`(1.741 \pm 0.069)\times10^{4}`$ | $`\pm 0.074\times10^{4}`$ | $`(1.757 \pm 0.101)\times10^{4}`$ | $`(2.096 \pm 0.41)\times10^{4}`$ |
+| Inertial-frame $`\lvert v \rvert`$ (km/s) | 307.1 ± 0.26 | ± 0.28 | 307.3 ± 0.34 | 333.6 ± 6.40 |
+| $`v_{R}`$ (km/s, inertial RTN) | 307.06 ± 0.26 | ± 0.27 | 307.18 ± 0.41 | +333.30 ± 6.40 |
+| $`v_{T}`$ (km/s, inertial RTN) | +3.71 ± 1.30 | ± 1.32 | +6.07 ± 2.20 | +12.70 ± 1.00 |
+| $`v_{N}`$ (km/s, inertial RTN) | -2.81 ± 1.37 | ± 1.39 | -3.79 ± 1.81 | -0.30 ± 1.00 |
+<!-- END: real_data_table -->
+
+The two SWAPI fits agree on every moment to well within their uncertainties.
+The reported HC3 uncertainties come from an approximate covariance matrix.
+The `boot σ` column reports an independent $`B = 300`$ nonparametric statistical bootstrap as a cross-check.
+
+The corresponding WIND/SWE ~2-min sample at 22:49:48 UT reports a faster, denser, hotter wind. WIND's velocity components are computed from its published GSE values via the L1-frame approximation $`v_{R} \approx -V_{x,\text{GSE}},\thinspace  v_{T} \approx -V_{y,\text{GSE}},\thinspace  v_{N} \approx V_{z,\text{GSE}}`$.
+Differences between the spacecraft measurements include systematic effects such as spatial separation and temporal variation of the solar wind.
+
+*The figure and table are generated by `docs/swapi/figure_src/plot_real_data_fit.py`.*
+
+## Input Data
+
+### L2 Science Data
+
+The primary input for `SwapiProcessor` is SWAPI L2 coincidence count-rate data (`imap_swapi_l2_sci`). Each CDF contains time-ordered ESA sweeps with fields:
+
+- `swp_coin_rate` — coincidence count rate (Hz) for each ESA step.
+- `esa_energy` — energy-per-charge setting for each ESA step. It is related to the instrument's actual ESA voltage setting by $`k_{\text{L2}} = 1.93`$ eV/V/e.
+- `sci_start_time` — sweep start epoch (TT2000 ns)
+
+#### ESA steps
+
+Each 12-second ESA sweep contains **72 ESA steps** (indices 0–71). Their roles are:
+
+| Indices | # Steps | Description |
+|---------|-------|-------------|
+| 0       | 1     | **Always discarded.** Voltage ramp-up step. |
+| 1–62    | 62    | **Coarse steps.** Fixed ESA voltage steps with logarithmic spacing. These steps are ordered from high to low voltage. |
+| 63–71   | 9     | **Fine steps.** Instrument mode dependent, but usually provides a higher-resolution scan of the proton peak, using smaller voltage steps. |
+
+To fit protons, we use both the coarse and fine steps, since the fine steps are usually concentrated near the proton peak.
+To fit alphas, we use only the coarse steps.
+
+Occasionally, one or more fine steps will have zero ESA voltage; these are excluded from the fit.
+
+#### Cadence
+
+The CDF provides these 12-second sweeps for one day per file.
+`SwapiProcessor` groups sweeps into non-overlapping 5-sweep chunks (60 s cadence), the least common multiple of the spin rate (approximately 15 s) and the sweep cadence (12 s).
+
+Using 5 sweeps makes it possible to determine the solar-wind bulk velocity.
+The solar-wind fitting algorithms operate on these 5-sweep chunks independently.
+
+### SPICE Kernels
+
+The processor requires SWAPI-to-RTN rotation matrices (one per ESA step) and IMAP's velocity in the Sun's reference frame from the SPICE kernels. The forward model uses the rotation to express look directions defined in instrument coordinates in the spacecraft RTN frame.
+
+The start time for each sweep, available from the L2 CDF, is denoted $`t_{\text{start}}`$.
+For ESA step $`i`$ (0-indexed, although recall that step 0 is skipped), the measurement time is:
+```math
+t_{i} = t_{\text{start}} + i \cdot \tfrac{12}{72}\thinspace \text{s} = t_{\text{start}} + i \cdot 0.1\overline{6}\thinspace \text{s}.
+```
+
+### MAG RTN (alpha only)
+
+The alpha moments depend on the local magnetic field direction because the alpha-proton drift is constrained to lie along the direction of the magnetic field ($`\hat{\mathbf{B}}`$).
+
+#### L2 vs L1D
+The dependency loader prefers MAG L2 and falls back to L1D when no L2 file is available. MAG is required for `alpha-sw`; the processor raises `ValueError` if neither product is provided, matching SWE's dependency loader behavior.
+
+When L1D is the source, every alpha-sw chunk in the run has its `PRELIMINARY_MAG` bit set so the product can be flagged for reprocessing once L2 is available. `proton-sw` and `pui-he` do not consume MAG.
+
+#### Averaging scheme
+
+For each 5-sweep alpha chunk, the processor uses the full $`60\thinspace \text{s}`$ MAG window $`[\thinspace t_{\text{center}} - 30\thinspace \text{s},\thinspace  t_{\text{center}} + 30\thinspace \text{s})`$.
+The in-window RTN samples are averaged directly, and the mean vector is normalized to produce $`\hat{\mathbf{B}}^{\text{RTN}}`$.
+
+#### Missing values
+
+If $`\hat{\mathbf{B}}^{\text{RTN}}`$ cannot be computed because there are no non-fill values within the window, the moments for that chunk are assigned fill values.
+
+## SWAPI Response Model
+
+The coincidence count rate at ESA voltage $`V`$ is
+```math
+C(V) = \sum_{s} \int d^3v \thinspace  v \thinspace  f^{s}(\mathbf{v}) \thinspace  \mathcal{A}^{s}(\mathbf{v}, V),
+```
+where $`f^{s}`$ is the VDF of species $`s`$ and $`\mathcal{A}^{s}`$ is the effective area.
+
+#### Effective area decomposition
+
+$`\mathcal{A}^{s}`$ is decomposed as
+```math
+\mathcal{A}^{s}(v, \theta, \phi, V) = \mathcal{A}_{0}^{s}(V) \cdot P\negthinspace \left(\dfrac{v}{v_{0}^{s}},\thinspace  \theta,\thinspace  \phi,\thinspace  V\right) \cdot \mathcal{T}(\phi),
+```
+where:
+- $`v_{0}^{s} = \sqrt{2 k^{\ast} q^{s} |V| / m^{s}}`$ is the central speed;
+- $`\mathcal{A}_{0}^{s}`$ is the central effective area;
+- $`P`$ is the energy-angle passband;
+- $`\mathcal{T}`$ is the azimuthal transmission factor.
+
+CSV versions of these three functions are in `instrument_team_data/swapi`.
+
+#### Normalization point
+
+The normalizations of $`\mathcal{A}_{0}^{s}`$ and $`P`$ are aligned in terms of the value at $`\theta = 0`$ and $`k^{\ast} \equiv 1.89`$ eV/V/e, the peak $`E/|V|`$ at $`\theta=0^{\circ}`$ based on high-resolution SIMION simulations.
+
+$`k^{\ast}`$ differs from $`k_{\text{L2}} = 1.93 eV/V/e`$, which is the $`k`$-factor estimated pre-launch from lab measurements (Rankin et al. 2025).
+They differ primarily because of small inaccuracies in the beam energy and orientation in the lab measurements.
+
+#### Tabulated form and grid construction
+
+`SwapiResponse` holds the in-memory representation of the three components of the response function: $`\mathcal{T}(\phi)`$, $`\mathcal{A}_{0}^{s}(V)`$, and the passband fit coefficients for $`P`$.
+
+$`\mathcal{T}(\phi)`$ and $`\mathcal{A}_{0}^{s}(V)`$ are 1D functions stored in simple CSV files. $`\mathcal{T}`$ is sampled at 0.1° spacing in $`|\phi|`$; $`\mathcal{A}_{0}^{s}`$ is sampled on the CSV voltage grid.
+Both are interpolated linearly between samples.
+$`\mathcal{A}_{0}^{s}`$ is clamped to its endpoints outside the tabulated voltage range.
+
+> ![](figures/calibration_curves.svg)
+> *Central effective area and azimuthal transmission.* [[src]](figure_src/plot_calibration_curves.py)
+
+$`P`$ at a given $`V`$ is represented as a `PassbandGrid` object.
+The CSV file contains quadratic polynomial fits of $`\log P`$ for each ($`\theta`$, $`v/v_{0}`$) pixel as a function of $`\log(k^{\ast} |V|)`$. Open aperture ($`|\phi| > 20°`$) and sunglasses ($`|\phi| \leq 20°`$) have separate fits.
+
+The central effective area is scaled using `EfficiencyCalibrationTable`, which stores detection efficiencies $`\varepsilon`$ for each species as a function of time.
+For protons, we scale it by ${\varepsilon_{p}(t)}/{\varepsilon_{p}(t_{\text{lab}})}$, where $\varepsilon_{p}(t_{\text{lab}})$ is the first proton entry in the table on or after 2025-11-01.
+For alphas, we scale it by ${\varepsilon_{\alpha}(t)}/{\varepsilon_{p}(t_{\text{lab}})}$, which accounts for the increased central effective area for alphas compared to protons.
+
+`SwapiResponse.create_passband_grid` evaluates these fits at the requested $`V`$ and resamples the speed-ratio axis onto a uniform grid: $`\theta`$ matches the CSV elevations ($`-15°`$ to $`15°`$ in $`0.5°`$ steps) and $`v/v_{0}`$ is resampled to $`0.9`$ to $`1.1`$ in 101 points. The resulting per-region grids are stored on `PassbandGrid` as `values_open_aperture` and `values_sunglasses` and used for bilinear interpolation in $`(\theta, v/v_{0})`$ inside the integrator. Voltages outside the fitted range are clamped to the nearest endpoint.
+
+![SWAPI passband and integration region at three beam energies](figures/passband_boundaries.svg)
+> *Example passbands.* [[src]](figure_src/plot_passband_boundaries.py)
+
+`PassbandGrid` also stores the passband region used by the integrator: a per-region elevation range (`oa_elevation_range`, `sg_elevation_range`) bounding the $`\theta`$ integration window, and per-elevation speed-ratio bounds (`min_OA_boundary`, `max_OA_boundary`, `min_SG_boundary`, `max_SG_boundary`) bounding the $`v`$ integration window for each elevation row inside that range.
+
+The integration region is set by a threshold of 1% of that region's grid maximum (computed independently for SG and OA). For each elevation row with at least one above-threshold cell, the speed-ratio bounds are the first speed-ratio pixels just outside the above-threshold region. Rows with no above-threshold cell are omitted.
+
+The elevation range is anchored at the interpolated crossing where the row maximum drops below threshold. Both the speed-ratio bounds and elevation range are recomputed for every $`V`$, since the polynomial fits change shape with voltage.
+
+When an integration elevation falls between stored passband-boundary rows, the wider neighboring interval is used. This avoids clipping the passband between rows.
+
+`SwapiProcessor` precomputes a `PassbandGrid` for each unique ESA voltage in an L2 file once, before fitting any of the 5-sweep chunks.
+At fit setup, each ESA voltage step is wrapped in a `ResponseGrid` that bundles the precomputed passband grid with the species-dependent central speed $`v_{0}`$, central effective area, and azimuthal transmission.
+
+## Forward Model
+
+#### Velocity Distribution Function
+
+The solar wind proton velocity distribution function (VDF) is modeled as a drifting Maxwellian, parameterized by the fit parameter vector $`\mathbf{x} = (\ln n,\thinspace  \ln T,\thinspace  v_{R},\thinspace  v_{T},\thinspace  v_{N})`$ — density and temperature in log-space, and bulk velocity $`\mathbf{v}_{b} = (v_{R}, v_{T}, v_{N})^{\top}`$ as linear RTN components. We work entirely in the RTN frame: the integration variable $`\mathbf{v}`$ is the particle velocity in RTN, and the bulk velocity $`\mathbf{v}_{b}`$ is also in RTN. No rotation of $`\mathbf{v}_{b}`$ into the instrument frame is needed at any point in the integral.
+
+The VDF, written as a function of $`\mathbf{v} \in \mathbb{R}^{3}`$:
+```math
+f^{p}(\mathbf{v}; \mathbf{x}) = \frac{n}{(\sqrt{2\pi}\thinspace  v_{\text{th}})^{3}} \exp\negthinspace \left(-\frac{|\mathbf{v} - \mathbf{v}_{b}|^{2}}{2 v_{\text{th}}^{2}}\right) = \frac{n}{(\sqrt{2\pi}\thinspace  v_{\text{th}})^{3}} \exp\negthinspace \left(-\frac{v^{2} - 2\thinspace v\thinspace (\hat{\mathbf{d}}\cdot\mathbf{v}_{b}) + v_{b}^{2}}{2 v_{\text{th}}^{2}}\right),
+```
+where $`v = |\mathbf{v}|`$, $`\hat{\mathbf{d}} = \mathbf{v}/v`$ is the flow direction unit vector (in RTN), $`v_{b} = |\mathbf{v}_{b}|`$, and $`v_{\text{th}} = \sqrt{k_{B} T/m_{p}}`$.
+
+The instrument samples $`f^{p}`$ over particle directions naturally defined by instrument-frame elevation and azimuth $`(\theta, \phi)`$. We convert to flow direction from instrument angular look direction coordinates (Rankin et al. 2025) as
+```math
+\hat{\mathbf{d}}^{\text{XYZ}}(\theta, \phi) = \bigl(-\cos\theta \sin\phi,\thinspace  -\cos\theta \cos\phi,\thinspace  -\sin\theta\bigr)^{\top}
+```
+and rotate it into RTN using the SWAPI→RTN rotation matrix $`R`$ for the corresponding ESA step:
+```math
+\hat{\mathbf{d}}(\theta, \phi) = R\thinspace \hat{\mathbf{d}}^{\text{XYZ}}(\theta, \phi).
+```
+
+#### Coincidence Rate Integral
+
+Substituting the VDF into the count rate integral in spherical velocity coordinates $`(v, \theta, \phi)`$:
+```math
+C(V) = \frac{n\thinspace  \mathcal{A}_{0}(V)}{(\sqrt{2\pi}\thinspace  v_{\text{th}})^{3}} \sum_{\text{region}} \int \cos\theta\thinspace  d\theta \int \mathcal{T}(\phi)\thinspace  d\phi \int v^{3}\thinspace  P\negthinspace \left(\tfrac{v}{v_{0}}, \theta\right) \exp\negthinspace \left(-\frac{v^{2} - 2\thinspace v\thinspace (\hat{\mathbf{d}}\cdot\mathbf{v}_{b}) + v_{b}^{2}}{2 v_{\text{th}}^{2}}\right) dv.
+```
+The $`v^{3}\cos\theta`$ factor comes from the velocity-space volume element $`v^{2}\cos\theta\thinspace dv\thinspace d\theta\thinspace d\phi`$ times the particle speed $`v`$ in the flux term.
+
+### Integration Method
+
+#### Azimuthal Regions
+
+The region sum runs over three azimuth regions: sunglasses (SG, $`|\phi| \leq 20°`$) and the two halves of the open aperture (OA, $`20° \leq |\phi| \leq 150°`$). Each region uses a single passband (SG or OA), and the SG/OA boundary at $`\pm 20°`$ is the natural split since $`\mathcal{T}(\phi)`$ is identically zero there (vanes fully blocking).
+
+#### Quadrature Method
+
+Each region is evaluated as a nested Gauss-Legendre quadrature with a fixed number of integration points:
+```math
+(N_{\theta}, N_{\phi}, N_{v}) = (21,\thinspace 21,\thinspace 15).
+```
+
+The loops are nested $`\theta \to \phi \to v`$, with terms that depend only on outer-loop variables hoisted out of the inner loops.
+
+#### Angular limits
+
+For each azimuth region, the angular cutoff $`\Delta\alpha`$ is chosen from the VDF angular falloff at the passband central speed $`v_{0}`$. At fixed speed $`v`$, the Maxwellian's angular dependence relative to its on-axis value is
+```math
+\frac{f(v, \alpha)}{f(v, 0)} = \exp\negthinspace \left(\frac{v v_{b} (\cos\alpha - 1)}{v_{\text{th}}^{2}}\right).
+```
+Setting this ratio to $`\varepsilon`$ at $`v = v_{0}`$ and solving for $`\alpha`$:
+```math
+\Delta\alpha = \frac{180}{\pi}\arccos\negthinspace \left(\mathrm{clamp}\negthinspace \left(\frac{v_{\text{th}}^{2} \ln\varepsilon}{v_{0} v_{b}} + 1;\thinspace  -1,\thinspace  1\right)\right),
+```
+with $`\varepsilon = 10^{-6}`$. If the VDF is broad enough that the arccos argument would leave $`[-1, 1]`$, the clamp makes $`\Delta\alpha = 180^{\circ}`$.
+
+The implementation uses this angular cutoff as a rectangular half-extent in instrument-frame elevation and azimuth around the bulk direction:
+```math
+[\theta_{b} - \Delta\alpha,\thinspace  \theta_{b} + \Delta\alpha] \times [\phi_{b} - \Delta\alpha,\thinspace  \phi_{b} + \Delta\alpha].
+```
+Here $`(\theta_{b}, \phi_{b})`$ are the elevation and azimuth of the bulk direction expressed in the instrument frame, obtained by mapping $`\mathbf{v}_{b}`$ into instrument coordinates via the SWAPI→RTN matrix transpose: $`\mathbf{v}_{b}^{\text{XYZ}} = R^{\top} \mathbf{v}_{b}`$, then $`\phi_{b} = \mathrm{arctan2}(-v_{b,x}^{\text{XYZ}},\thinspace  -v_{b,y}^{\text{XYZ}})`$ and $`\theta_{b} = \arcsin(-v_{b,z}^{\text{XYZ}}/v_{b})`$. This rectangle is conservative: it contains the angular disk of radius $`\Delta\alpha`$ around the bulk direction.
+
+The window is then clamped to the passband elevation range for the region and to that region's azimuth span:
+
+| Region | Azimuth Range |
+|--------|----------------|
+| SG     | $`[-20°,\thinspace  20°]`$ |
+| OA−    | $`[-150°,\thinspace  -20°]`$ |
+| OA+    | $`[20°,\thinspace  150°]`$ |
+
+If either clamped dimension has zero width, that region is skipped.
+
+For OA± only, the azimuth window is trimmed once more using the product of the VDF and azimuthal transmission. `_trim_oa_azimuth_by_integrand` samples 64 points of $`f(v_{0}, \theta_{b}', \phi)\mathcal{T}(\phi)`$ across the clamped OA azimuth window, where $`\theta_{b}'`$ is $`\theta_{b}`$ clamped into the OA elevation range. It keeps the portion above $`10^{-6}`$ of its maximum and expands by one sample on each side.
+
+After this trim, OA $`\pm`$ is skipped when the heuristic upper estimate
+```math
+\hat{C}_{\text{OA}} = \mathcal{A}_{0}(V)\thinspace v_{0}^{3}\thinspace \Delta\theta\thinspace \Delta v\thinspace \int_{\phi_{\text{lo}}}^{\phi_{\text{hi}}} \mathcal{T}(\phi)\thinspace g(\phi)\thinspace d\phi
+```
+falls below $`\max(0.1\thinspace \text{Hz},\thinspace  10^{-3} C_{\text{SG}})`$. Here $`g(\phi) = f(v_{0}, \theta_{b}', \phi)`$, $`\Delta\theta`$ is the clamped OA elevation width in radians, and $`\Delta v = (r_{\text{max}}(0) - r_{\text{min}}(0))v_{0}`$ is the OA passband speed width at $`\theta = 0^{\circ}`$.
+
+#### Speed limits
+
+For each Gauss-Legendre elevation node, the speed integral only needs to cover speeds where both of these are true:
+
+1. The VDF is non-negligible.
+2. The SWAPI passband is nonzero at that elevation.
+
+The VDF speed interval is taken to be
+```math
+[v_{b} - \Delta v_{\text{VDF}},\thinspace  v_{b} + \Delta v_{\text{VDF}}], \qquad \Delta v_{\text{VDF}} = 6v_{\text{th}},
+```
+where $`v_{b} = |\mathbf{v}_{b}|`$ and $`v_{\text{th}}`$ is the thermal speed. For the Maxwellian VDF used here, this captures essentially all of the distribution: at $`6\sigma`$ the radial factor is $`e^{-18} \approx 10^{-8}`$ of peak. A much wider window (e.g., $`10v_{\text{th}}`$) makes Gauss-Legendre concentrate nodes far from the integrand peak for cold plasma where the passband already extends well beyond $`6v_{\text{th}}`$; a much narrower one (e.g., $`3v_{\text{th}}`$) clips the Maxwellian wings too much for off-peak passband alignments.
+
+The passband speed range is stored as speed-ratio bounds relative to the central passband speed $`v_{0}`$:
+```math
+[r_{\text{min}}(\theta)v_{0},\thinspace  r_{\text{max}}(\theta)v_{0}].
+```
+Here $`r_{\text{min}}(\theta)`$ and $`r_{\text{max}}(\theta)`$ depend on both elevation and aperture region (SG or OA). They describe where the passband at that elevation remains above the integration cutoff.
+
+The speed integration limits are the intersection of those two windows:
+```math
+v_{\text{lo}}(\theta) = \max\negthinspace \left(v_{b} - \Delta v_{\text{VDF}},\thinspace  r_{\text{min}}(\theta)v_{0}\right),
+```
+```math
+v_{\text{hi}}(\theta) = \min\negthinspace \left(v_{b} + \Delta v_{\text{VDF}},\thinspace  r_{\text{max}}(\theta)v_{0}\right).
+```
+
+If the interval $`[v_{b} - \Delta v_{\text{VDF}},\thinspace  v_{b} + \Delta v_{\text{VDF}}]`$ does not overlap with $`[0.9\thinspace v_{0},\thinspace  1.1\thinspace v_{0}]`$, the integral is skipped early because there it is guaranteed to be zero.
+
+### Integrator Validation
+
+The optimized integrator (`calculate_integral`) is validated against a high-resolution fixed-limit reference integrator (`reference_integral_fixed_limits`) — the same forward model evaluated on a much denser fixed grid with no dynamic integration limits.
+
+The figure below compares the two integrators on six solar wind configurations: cold and hot temperatures, bulk elevation past the SG passband edge, bulk azimuth straddling the SG/OA boundary, and high speed.
+
+![Production vs ground-truth spectra for six representative SW configurations](figures/spectra.svg)
+
+*Generated by `docs/swapi/figure_src/plot_spectra.py`.*
+
+For aggregate accuracy, the optimized integrator is evaluated against the same reference integral over 10,000 random solar-wind configurations (`reference_integrals.csv`).
+Each configuration is evaluated at the ESA voltage whose central proton speed equals its `bulk_speed`.
+The table below summarizes the distribution of $`|\text{ratio} - 1|`$ grouped by reference coincidence rate.
+
+<!-- BEGIN: validation_table (auto-generated by docs/swapi/figure_src/build_validation_table.py — do not edit by hand) -->
+| Reference (Hz)  |     N |  Median |     95% |     99% |     Max |
+|-----------------|-------|---------|---------|---------|---------|
+| $\lt 0.1$       |   228 |  26.25% |  84.63% |  97.49% | 100.00% |
+| $0.1$ – $1$     |   239 |   7.07% |  25.48% |  38.48% |  41.93% |
+| $1$ – $10$      |   325 |   2.76% |  14.03% |  22.15% |  28.74% |
+| $10$ – $10^2$   |   383 |   1.40% |   8.98% |  14.46% |  17.61% |
+| $10^2$ – $10^3$ |   525 |   0.81% |   4.56% |   9.46% |  16.15% |
+| $10^3$ – $10^4$ |   865 |   0.41% |   2.14% |   5.18% |  11.28% |
+| $10^4$ – $10^5$ |  1694 |   0.19% |   0.85% |   1.70% |   3.38% |
+| $\geq 10^5$     |  3841 |   0.14% |   0.63% |   0.98% |   2.21% |
+<!-- END: validation_table -->
+*Generated by `docs/swapi/figure_src/build_validation_table.py`.*
+
+For high-rate cases ($`\geq 10^{3}`$ Hz) where proton-fit residuals are dominated by Poisson noise rather than integrator error, $`|\text{ratio} - 1|`$ stays within a few percent of unity at the 99th percentile. The $`< 0.1`$ Hz band is configurations where the bulk direction sits many sigma outside the FOV — both integrators round to well below SWAPI's noise floor (which varies between 0.1 Hz and 10 Hz, typically closer to 10 Hz), so the ratio is clamped to $`100\%`$.
+The worst cases at typical solar wind coincidence rate ($`\geq 10^{4}`$ Hz) are primarily due to bulk flow directions near the edge of the instrument response, which is rare by design because of the alignment of SWAPI's boresight and the spacecraft spin axis with the nominal average solar wind direction.
+
+### Analytic Jacobian
+
+Since $`C(V)`$ is linear in $`f^{p}(\mathbf{v}; \mathbf{x})`$ and $`\mathcal{A}(\mathbf{v}, V)`$ is independent of $`\mathbf{x}`$,
+```math
+\frac{\partial C(V)}{\partial x_{j}} = \frac{\partial}{\partial x_{j}}\int d^3v\thinspace v\thinspace f^{p}(\mathbf{v}; \mathbf{x})\thinspace \mathcal{A}(\mathbf{v}, V) = \int d^3v\thinspace v\thinspace \frac{\partial f^{p}}{\partial x_{j}}(\mathbf{v}; \mathbf{x})\thinspace \mathcal{A}(\mathbf{v}, V).
+```
+The same integral used for $`C`$ then delivers all five Jacobian columns in one pass at minimal cost.
+The rest of this section derives $`\partial f^{p}/\partial x_{j}`$ for each component of $`\mathbf{x}`$.
+Working entirely in the RTN frame (so $`\mathbf{v}`$ and $`\mathbf{v}_{b}`$ are both 3-vectors in RTN), the log of the Maxwellian is
+```math
+\ln f^{p} = \ln n - \tfrac{3}{2}\ln v_{\text{th}}^{2} - \frac{|\mathbf{v} - \mathbf{v}_{b}|^{2}}{2\thinspace v_{\text{th}}^{2}} + \text{const}.
+```
+Expanding the squared offset, $`|\mathbf{v} - \mathbf{v}_{b}|^{2} = v^{2} - 2\thinspace v\thinspace (\hat{\mathbf{d}}\cdot\mathbf{v}_{b}) + v_{b}^{2}`$, where $`\hat{\mathbf{d}}`$ is the look direction in RTN at the integration node (built in instrument XYZ from $`(\theta, \phi)`$ and rotated by $`R`$).
+
+#### Density
+
+$`f^{p}`$ is linear in $`n`$, so
+```math
+\frac{\partial f^{p}}{\partial n} = \frac{f^{p}}{n}.
+```
+
+Converting to log-space via $`\partial f^{p}/\partial \ln n = n\thinspace \partial f^{p}/\partial n`$,
+```math
+\frac{\partial f^{p}}{\partial \ln n} = f^{p}.
+```
+
+#### Temperature
+
+$`T`$ enters $`f^{p}`$ only through $`v_{\text{th}}^{2} = k_{B} T/m`$, so we first differentiate with respect to $`v_{\text{th}}^{2}`$ and then convert. From $`\ln f^{p}`$,
+```math
+\frac{\partial \ln f^{p}}{\partial v_{\text{th}}^{2}} = -\frac{3}{2\thinspace v_{\text{th}}^{2}} + \frac{|\mathbf{v} - \mathbf{v}_{b}|^{2}}{2\thinspace v_{\text{th}}^{4}}.
+```
+The first term comes from the $`-\tfrac{3}{2}\ln v_{\text{th}}^{2}`$ coefficient; the second from differentiating the exponent's $`1/v_{\text{th}}^{2}`$.
+
+The remaining steps are three applications of the same change-of-variables identity,
+```math
+\frac{\partial g}{\partial y} = \frac{\partial g}{\partial x}\cdot\frac{\partial x}{\partial y},
+```
+in the sequence $`v_{\text{th}}^{2} \to T`$, $`\ln f^{p} \to f^{p}`$, and $`T \to \ln T`$.
+
+First:
+```math
+\frac{\partial \ln f^{p}}{\partial T} = \frac{\partial v_{\text{th}}^{2}}{\partial T} \cdot \frac{\partial \ln f^{p}}{\partial v_{\text{th}}^{2}} = \frac{v_{\text{th}}^{2}}{T}\thinspace \frac{\partial \ln f^{p}}{\partial v_{\text{th}}^{2}} = \frac{1}{T} \cdot \left(\frac{|\mathbf{v} - \mathbf{v}_{b}|^{2}}{2\thinspace v_{\text{th}}^{2}} - \tfrac{3}{2}\right).
+```
+
+Then:
+```math
+\frac{\partial f^{p}}{\partial T} = \frac{f^{p}}{T}\cdot\left(\frac{|\mathbf{v} - \mathbf{v}_{b}|^{2}}{2\thinspace v_{\text{th}}^{2}} - \tfrac{3}{2}\right).
+```
+
+Finally:
+```math
+\frac{\partial f^{p}}{\partial \ln T} = f^{p}\cdot\left(\frac{|\mathbf{v} - \mathbf{v}_{b}|^{2}}{2\thinspace v_{\text{th}}^{2}} - \tfrac{3}{2}\right).
+```
+
+The sign reverses across $`|\mathbf{v} - \mathbf{v}_{b}|^{2} = 3\thinspace v_{\text{th}}^{2}`$: increasing $`T`$ decreases $`f^{p}`$ for $`v \approx v_{b}`$ and increases it for $`v \gg v_{b}`$.
+
+#### Bulk velocity components
+
+Because $`\mathbf{v}_{b}`$ enters $`f^{p}`$ only through the squared offset $`|\mathbf{v} - \mathbf{v}_{b}|^{2}`$ in the exponent, and both $`\mathbf{v}`$ and $`\mathbf{v}_{b}`$ are vectors in the *same* (RTN) frame, the gradient with respect to $`\mathbf{v}_{b}`$ is direct — no rotation matrices appear:
+```math
+\nabla_{\mathbf{v}_{b}}\negthinspace \left[\tfrac{1}{2}|\mathbf{v} - \mathbf{v}_{b}|^{2}\right] = -(\mathbf{v} - \mathbf{v}_{b}).
+```
+Factoring out the constant $`-1/v_{\text{th}}^{2}`$ from $`\ln f^{p}`$,
+```math
+\nabla_{\mathbf{v}_{b}}\ln f^{p} = \frac{1}{v_{\text{th}}^{2}}\thinspace (\mathbf{v} - \mathbf{v}_{b}).
+```
+Multiplying by $`f^{p}`$ and writing the integration-variable velocity as $`\mathbf{v} = v\thinspace \hat{\mathbf{d}}`$ (where $`\hat{\mathbf{d}}`$ is the look direction in RTN):
+```math
+\nabla_{\mathbf{v}_{b}} f^{p} = \frac{f^{p}}{v_{\text{th}}^{2}}\thinspace \bigl(v\thinspace \hat{\mathbf{d}} - \mathbf{v}_{b}\bigr).
+```
+The three components are
+```math
+\frac{\partial f^{p}}{\partial v_{R}} = \frac{f^{p}}{v_{\text{th}}^{2}}\thinspace \bigl(v\thinspace \hat{d}_{R} - v_{R}\bigr), \qquad \frac{\partial f^{p}}{\partial v_{T}} = \frac{f^{p}}{v_{\text{th}}^{2}}\thinspace \bigl(v\thinspace \hat{d}_{T} - v_{T}\bigr), \qquad \frac{\partial f^{p}}{\partial v_{N}} = \frac{f^{p}}{v_{\text{th}}^{2}}\thinspace \bigl(v\thinspace \hat{d}_{N} - v_{N}\bigr).
+```
+
+#### Boundary terms
+
+Technically, the integration limits that we use are themselves functions of $`(v_{b}, v_{\text{th}})`$ via the [angular limits](#angular-limits) and [speed limits](#speed-limits).
+But for our analytic Jacobian, we hold the limits fixed at the current estimate and ignore the boundary terms.
+This is the same fixed-limit approximation used when evaluating the coincidence rate itself.
+
+### Deadtime correction
+
+The detector deadtime is $`\tau = 183.7\ \text{ns}`$. Following Tsoulfanidis (1995), p. 74, the true count rate $`n`$ and measured rate $`g`$ are related by $`n = g / (1 - g\tau)`$. Rearranged for the forward model, the true model rate $`C^{\text{model}}`$ is mapped to the predicted observed rate before computing residuals:
+```math
+C^{\text{observed}}_{i} = \frac{C^{\text{model}}_{i}}{1 + \tau\thinspace  C^{\text{model}}_{i}} = C^{\text{model}}_{i} \cdot \mathcal{D}_{i}, \qquad \mathcal{D}_{i} \equiv \frac{1}{1 + \tau\thinspace  C^{\text{model}}_{i}}.
+```
+This deadtime correction is often non-negligible. It reaches 5% at $`C \approx 2.7\times 10^{5}\ \text{Hz}`$, a routine value for high-flux solar wind. Skipping it would overestimate the predicted observed rate and underestimate the fitted density.
+
+Because deadtime depends on $`C^{\text{model}}`$, it propagates into the residual Jacobian by the chain rule. Differentiating $`C^{\text{observed}} = C^{\text{model}}/(1 + \tau\thinspace C^{\text{model}})`$ with respect to $`C^{\text{model}}`$ via the quotient rule,
+```math
+\frac{\partial C^{\text{observed}}}{\partial C^{\text{model}}} = \frac{(1+\tau\thinspace C^{\text{model}}) - C^{\text{model}}\cdot\tau}{(1+\tau\thinspace C^{\text{model}})^{2}} = \frac{1}{(1+\tau\thinspace C^{\text{model}})^{2}} = \mathcal{D}^{2}.
+```
+Applying the chain rule, the model-level Jacobian columns $`\partial C^{\text{model}}_{i}/\partial x_{j}`$ are scaled by $`\mathcal{D}_{i}^{2}`$ to obtain the observed-rate Jacobian:
+```math
+\frac{\partial C^{\text{observed}}_{i}}{\partial x_{j}} = \frac{\partial C^{\text{observed}}_{i}}{\partial C^{\text{model}}_{i}}\cdot\frac{\partial C^{\text{model}}_{i}}{\partial x_{j}} = \mathcal{D}_{i}^{2} \cdot \frac{\partial C^{\text{model}}_{i}}{\partial x_{j}}.
+```
+
+## Proton Fitting Procedure
+
+### Fit preparation
+
+Before processing the 5-sweep chunks in parallel, the parent process uses the SPICE kernels to obtain the SWAPI-to-RTN rotation matrix for each measurement,
+along with the spacecraft velocity in the Sun's reference frame at the center epoch of each chunk.
+
+If the rotation matrices are unavailable, the chunk is skipped and every output is assigned a fill value, since the forward model cannot be evaluated without per-bin look directions in RTN.
+If only the spacecraft velocity is unavailable, the fit is still performed but the `sun`-frame outputs are assigned fill values.
+
+### Initial Guess
+
+#### Initial bulk speed and temperature
+
+The initial guess for bulk speed and temperature is obtained as follows:
+1. Let $`v_{b}^{(0)}`$ be the speed corresponding to the ESA voltage (related to speed through $`k^{\ast}`$) of the measurement with the highest coincidence rate.
+2. Set the temperature based on the relationship used in the I-ALiRT code derived from the typical temperature as a function of bulk speed:
+
+```math
+T^{(0)} = \text{max}\negthinspace \left(1\thinspace \text{eV}, 60{,}000\thinspace \text{K} \cdot \left(\dfrac{v_{b}^{(0)}}{400\thinspace \text{km/s}}\right)^{2}\right).
+```
+
+3. Refine $`v_{b}^{(0)}`$ and $`T^{(0)}`$ through an arbitrarily normalized Gaussian fit (with $`\sigma_{v}`$ related to $`T`$ through $`T = m\sigma_{v}^{2} / k_{B}`$).
+The temperature output of the fit is clamped to be at least $`1\thinspace \text{eV}`$. If the initial guess fails, `FIT_ERROR` is set.
+
+#### Initial velocity direction
+
+Let $`\hat{\mathbf{s}}^{\text{RTN}}`$ be the chunk's spin axis in RTN, taken as the unit-normalized mean of the per-sweep body $`+\hat{\mathbf{Y}}`$ axes (the second column of each SWAPI→RTN rotation matrix — i.e. the image of SWAPI $`\hat{\mathbf{Y}}`$ under $`R`$, corresponding to SWAPI's boresight, which is parallel to the spin axis). The velocity is seeded anti-parallel to this axis:
+```math
+\mathbf{v}_{b}^{\text{SC,(0)}} = -v_{b}\thinspace \hat{\mathbf{s}}^{\text{RTN}}.
+```
+Because of IMAP's consistent orientation, the solar-wind bulk velocity is close to $`\hat{\mathbf{s}}^{\text{RTN}}`$ on average.
+
+#### Initial density
+
+The initial density is set by a fit of the unit-density ideal forward-model coincidence rate with deadtime correction applied against the observed count rates:
+```math
+n_{0} = \arg\min_{n} \sum_{i} \left[\thinspace  n\thinspace m_{i}\thinspace \mathcal{D}(n\thinspace m_{i}) - C_{i} \thinspace \right]^{2}, \qquad m_{i} = C_{i}^{\text{model}}(n = 1; T_{0}, \mathbf{v}_{b}^{\text{SC,(0)}}).
+```
+Because $`\mathcal{D}`$ is non-linear in $`n`$, the closed-form linear estimate $`n_{0}^{\text{lin}} = \mathbf{m}\cdot\mathbf{C}/(\mathbf{m}\cdot\mathbf{m})`$ systematically underestimates the density at typical SWAPI peak rates and is instead used as the starting point for this fit, which is handled using SciPy `curve_fit`.
+
+### Least-Squares Fitting Procedure
+
+The state vector is
+```math
+\mathbf{x} = [\log n,\thinspace  \log T,\thinspace  v_{R},\thinspace  v_{T},\thinspace  v_{N}],
+```
+with $`\mathbf{v}_{b}^{\text{SC}} = (v_{R}, v_{T}, v_{N})`$ in the spacecraft RTN frame. Density and temperature are parameterized in log-space to keep them positive throughout optimization.
+
+The fit uses `scipy.optimize.least_squares` with the Levenberg–Marquardt method (`method='lm'`, `xtol=1e-4`). Residuals are unweighted over all retained bins:
+```math
+r_{i} = C_{i}^{\text{observed}} - C_{i},
+```
+where $`C_{i}`$ is the measured count rate and $`C_{i}^{\text{observed}}`$ is the deadtime-corrected predicted rate (see [Deadtime correction](#deadtime-correction)).
+
+We use unweighted residuals rather than commonly used inverse-variance weighting. Poisson variance is proportional to count rate, so inverse-variance weighting would up-weight the low-count bins. Those bins are where non-Maxwellian populations — the proton shoulder, pickup ions, and alpha particles — contribute most. These populations are also more isotropic than the proton core, and are therefore magnified relative to the proton core by SWAPI's sunglasses, making them especially important for SWAPI compared with other spacecraft instruments.
+Using unweighted residuals keeps the fit focused on the high-count bins where the proton core is located and where the count rate is high enough that Poisson variability is negligible, so inverse-variance weighting is unhelpful.
+
+For each residual evaluation, the look direction $`\hat{\mathbf{d}}`$ is built in instrument XYZ from each $`(\theta, \phi)`$ Gauss-Legendre node and rotated into RTN per measurement using the SWAPI→RTN matrix $`R_{i}`$:
+```math
+\hat{\mathbf{d}}_{i} = R_{i}\thinspace \hat{\mathbf{d}}^{\text{XYZ}}(\theta, \phi).
+```
+The exponent in the Maxwellian then uses the dot product $`\hat{\mathbf{d}}_{i} \cdot \mathbf{v}_{b}^{\text{SC}}`$ directly. The angular *integration limits* still use bulk-direction angles in the instrument frame, computed from $`\mathbf{v}_{b}^{\text{XYZ}} = R_{i}^{\top} \mathbf{v}_{b}^{\text{SC}}`$ (Rankin et al. 2025):
+```math
+\phi_{b,i} = \mathrm{arctan2}(-v_{b,i,x}^{\text{XYZ}},\thinspace  -v_{b,i,y}^{\text{XYZ}}), \qquad \theta_{b,i} = \arcsin\negthinspace \left(-\frac{v_{b,i,z}^{\text{XYZ}}}{v_{b}}\right).
+```
+These angles bound the rectangular integration window described in [Angular limits](#angular-limits); they do not enter the integrand itself.
+
+#### Wrong-basin detection (iterative spin-axis flip)
+
+The fitting landscape often has a local minimum in bulk velocity related by a 180° rotation of the bulk velocity about the spin axis $`\hat{\mathbf{s}}^{\text{RTN}}`$.
+The two minima are not degenerate, because the sunglasses (SG) passband has a slightly asymmetric elevation range ($`[-10.5^{\circ}, +7^{\circ}]`$) and an elevation-dependent speed response, causing the bulk speed to shift from one sweep to the next.
+The flipped solution is only a local minimum and usually has $`\chi^{2}`$ about $`100`$ to $`500\times`$ larger than the global minimum.
+
+The first fit returns bulk velocity $`\mathbf{v}_{b}^{(0)}`$, density $`n^{(0)}`$, temperature $`T^{(0)}`$, and $`\text{MSE}^{(0)}`$.
+At iteration $`k`$ (up to 6 times):
+
+1. **Build a flipped seed.** Compute the 180°-rotated bulk velocity
+   ```math
+   \mathbf{v}_{b}^{\text{flip}} = 2\thinspace \hat{\mathbf{s}}\thinspace (\hat{\mathbf{s}}\cdot\mathbf{v}_{b}^{(k)}) - \mathbf{v}_{b}^{(k)},
+   ```
+   and rescale the density at $`(\mathbf{v}_{b}^{\text{flip}}, T^{(k)})`$, giving $`n^{\text{flip}}`$ and $`\text{MSE}_{\text{flip}}`$. The rescale is needed because flipping the velocity makes $`n^{(k)}`$ no longer near-optimal.
+2. **Check threshold.** If $`\text{MSE}_{\text{flip}} \geq 100\thinspace \text{MSE}^{(k)}`$ (RMSE ratio $`\geq 10`$), terminate the loop.
+3. **Repeat.** Otherwise, run a full least-squares fit seeded from $`(\mathbf{v}_{b}^{\text{flip}}, n^{\text{flip}}, T^{(k)})`$. If its MSE is no worse than $`\text{MSE}^{(k)}`$, it becomes iteration $`k+1`$ and the loop continues; otherwise terminate.
+
+### Fitting Algorithm Validation
+
+To validate that the algorithm recovers solar-wind moments under realistic conditions, we performed the following experiment:
+
+1. Sample 10,000 real solar-wind cases from WIND/SWE 2-min measurements from 2025.
+2. Build the per-fit voltage and pointing geometry: 5 sweeps over the 62 coarse ESA voltage steps (leaving out the fine sweeps, which would improve the accuracy of the fit if available).
+3. Generate per-bin SWAPI→RTN rotation matrices by spinning a representative rotation matrix about the spin axis at a 15.13 s spin period.
+4. Forward model the count rate from the ground truth at each bin, apply the deadtime correction, and sample Poisson noise scaled by the per-bin dwell time (~0.145 s).
+5. Apply the full fitting algorithm to those synthetic rates and compare the recovered moments against the ground truth.
+
+![Initial-guess vs. final-fit accuracy for 10000 real WIND/SWE solar wind cases](figures/fit_accuracy.svg)
+
+*Generated by `docs/swapi/figure_src/plot_fit_accuracy.py`.*
+
+### Uncertainty Estimation
+
+The proton fit uses unweighted least squares to keep non-core populations from driving the fit.
+Even when the true variance of the residuals is not uniform (heteroscedasticity), unweighted least squares (which is optimal under homoscedasticity) is still unbiased (albeit not maximally efficient).
+However, the usual covariance matrix from unweighted least-squares routines does not share that property here.
+To correct for this, we use the heteroscedasticity-consistent HC3 method (MacKinnon & White, 1985; Long & Ervin, 2000; Hayes & Cai, 2007):
+```math
+\Sigma_{x} = (J^{\top} J)^+ \thinspace  J^{\top} \mathrm{diag}\!\Big(\tfrac{r_{i}^{2}}{(1 - h_{ii})^{2}}\Big)\thinspace  J\thinspace  (J^{\top} J)^+,
+```
+where $`J`$ is the analytic Jacobian of the residuals at the solution, $`r_{i} = \text{pred}_{i} - \text{obs}_{i}`$ is the per-bin residual at the converged parameters, $`h_{ii} = J_{i}\thinspace (J^{\top} J)^+ J_{i}^{\top}`$, and $`{}^+`$ represents the Moore–Penrose pseudoinverse (used for numerical stability).
+
+The figure below validates the uncertainty estimate empirically.
+One typical moderate-speed ground truth ($`n = 5\thinspace \text{cm}^{-3}`$, $`T = 10^{5}\thinspace \text{K}`$, $`\mathbf{v}_{\text{RTN}} = (450, 5, -3)\thinspace \text{km/s}`$) is held fixed and noise is varied across 1000 synthetic 5-sweep chunks under two noise models. The top block uses pure Poisson noise on the underlying counts.
+The bottom block adds 1% multiplicative log-normal noise on top of the Poisson sample and adds an energy-independent 10 Hz Poisson noise floor at every bin.
+
+Within each block, the upper row shows histograms of the fitted parameters: each width is the true sampling sigma, the dashed line marks the truth, and the in-axis annotation reports the fit bias (mean − truth) in units of the empirical sigma. The lower row is the histogram of normalized residuals $`(\hat{\theta} - \theta_{\text{truth}}) / \hat{\sigma}`$ for two estimators: orange = HC3 (used here), green = the textbook uncorrected formula $`\Sigma_{x} = \big(\sum_{i} r_{i}^{2} / (N-K)\big) \cdot (J^{\top} J)^+`$ (shown for comparison).
+The dashed black curve is $`\mathcal{N}(0, 1)`$, the ideal normalized residual distribution.
+
+<img src="figures/uncertainty_mc.svg" alt="Monte Carlo validation: distribution of fitted parameters vs. distribution of estimated sigmas across 1000 noise realizations of one fixed solar-wind ground truth, under Poisson and Poisson+log-normal noise" width="1200">
+
+*Generated by `docs/swapi/figure_src/plot_uncertainty_mc.py`.*
+
+> **Important**: The uncertainties primarily represent statistical error and do not necessarily take systematic error into account.
+> For example, density relies on the central effective area calibration, which has an uncertainty of up to 20%.
+> This introduces systematic error that applies equally to all density measurements.
+
+### Failed fits
+
+After the optimizer returns, the moments are assigned fill values if the optimizer did not report `success`, in which case the flag `FIT_ERROR` is set.
+Fill values are also assigned if the fitted temperature exceeds $`5\times 10^{5} \, \text{K}`$, or the coefficient of determination $R^2$ is below $`0.9`$. In both cases, the flag `BAD_FIT` is set.
+
+The $R^2$ is computed on the count rate averaged across the chunk's 5 sweeps, using only the 9 ESA steps within ±4 of the peak in the averaged spectrum (coarse-sweep bins only). We average across sweeps before computing $R^2$ because individual sweeps have poorer $R^2$ due to the combination of solar wind temporal variation and spin variation, even when the fit is reasonable.
+
+### Peak speed fallback
+
+Whenever we report fill values, we attempt to populate `proton_sw_speed` based on the observed peak of the distribution.
+To obtain this estimate, we:
+1. Average each chunk's coincidence rate across its 5 sweeps.
+2. Find the peak coarse step by `argmax`, clamped so the averaging window has at least 4 steps on each side.
+3. Compute the count-rate-weighted average ESA voltage across this window and convert it to speed units using $k^*$.
+
+In some cases, `proton_sw_speed` may still have a fill value if this peak-finding procedure fails (e.g., data gap).
+
+### CDF Outputs
+
+Let $`\mathbf{x} = [\log n,\thinspace  \log T,\thinspace  v_{R},\thinspace  v_{T},\thinspace  v_{N}]`$ be the fitted parameter vector with covariance $`\Sigma_{x}`$, $`\mathbf{v}_{b}^{\text{SC}} = (x_{2}, x_{3}, x_{4})`$ the bulk velocity in the SC frame (RTN basis), and $`\mathbf{v}_{\text{sc}}^{\text{sun}}`$ the IMAP inertial velocity at the chunk-center epoch in the RTN basis<sup>[1](#fn-vsc)</sup>. The proton-sw L3A CDF variables are:
+
+| CDF variable                                  | Symbol                  | Definition                                                                                            |
+|-----------------------------------------------|-------------------------|-------------------------------------------------------------------------------------------------------|
+| `epoch`                                       | $`t_{\text{center}}`$       | center time of the 5-sweep chunk (TT2000 ns)                                                          |
+| `epoch_delta`                                 |                         | $`30\thinspace \text{s}`$ (half-width of the 5-sweep chunk, in ns)                                               |
+| `proton_sw_density`                           | $`n`$                     | $`\exp(x_{0})`$                                                                                           |
+| `proton_sw_density_uncert`                    | $`\sigma_{n}`$              | $`n\thinspace \sqrt{\Sigma_{x,00}}`$                                                                             |
+| `proton_sw_temperature`                       | $`T`$                     | $`\exp(x_{1})`$                                                                                           |
+| `proton_sw_temperature_uncert`                | $`\sigma_{T}`$              | $`T\thinspace \sqrt{\Sigma_{x,11}}`$                                                                             |
+| `proton_sw_bulk_velocity_rtn_sc`              | $`\mathbf{v}_{b}^{\text{SC}}`$ | $`(x_{2},\thinspace  x_{3},\thinspace  x_{4})`$                                                                                |
+| `proton_sw_bulk_velocity_rtn_sc_covariance`   | $`\Sigma_{\mathbf{v}}`$              | $`\Sigma_{x}[2{:}5,\thinspace 2{:}5]`$ <sup>[2](#fn-velcov)</sup>                                                  |
+| `proton_sw_speed`                             | $`\lvert\mathbf{v}_{b}^{\text{SC}}\rvert`$ | $`\lvert\mathbf{v}_{b}^{\text{SC}}\rvert`$                                                                  |
+| `proton_sw_speed_uncert`                      | $`\sigma_{\lvert\mathbf{v}_{b}^{\text{SC}}\rvert}`$ | propagated through `uncertainties` from $`\mathbf{v}_{b}^{\text{SC}}`$                            |
+| `proton_sw_bulk_velocity_rtn_sun`             | $`\mathbf{v}_{b}^{\text{sun}}`$ | $`\mathbf{v}_{b}^{\text{SC}} + \mathbf{v}_{\text{sc}}^{\text{sun}}`$                                          |
+| `proton_sw_bulk_velocity_rtn_sun_covariance`  | $`\Sigma_{\mathbf{v}}^{\text{sun}}`$   | $`\Sigma_{\mathbf{v}}`$ <sup>[3](#fn-sunframe)</sup>                                                               |
+| `proton_sw_speed_sun`                         | $`v_{\text{sun}}`$          | $`\lvert\mathbf{v}_{b}^{\text{sun}}\rvert`$                                                                 |
+| `proton_sw_speed_sun_uncert`                  | $`\sigma_{v_{\text{sun}}}`$ | propagated through `uncertainties` from $`\mathbf{v}_{b}^{\text{SC}}`$ + exact offset<sup>[3](#fn-sunframe)</sup> |
+| `swp_flags`                                   |                         | per-chunk quality bitmask (`SwapiL3Flags`)                                                            |
+
+When the fit fails (non-finite $`\Sigma_{x}`$), every `_uncert` and `_covariance` variable is reported as fill.
+
+<a id="fn-vsc"></a>[1]: $`\mathbf{v}_{\text{sc}}^{\text{sun}}`$ is the inertial (Sun-frame) IMAP velocity from SPICE's `imap_state(et, ECLIPJ2000)`, with its components rotated into the RTN axes at the chunk-center epoch.
+
+<a id="fn-velcov"></a>[2]: The bulk velocity is represented internally as a 3-tuple of correlated `uncertainties.UFloat` components carrying $`\Sigma_{\mathbf{v}}`$. The square root of the diagonal of $`\Sigma_{\mathbf{v}}`$ yields the standard error of the $`v_{R}, v_{T}, v_{N}`$ components.
+
+<a id="fn-sunframe"></a>[3]: Since $`\mathbf{v}_{\text{sc}}^{\text{sun}}`$ is SPICE-derived and treated as exact, the Sun-frame vector covariance equals the SC-frame velocity covariance. The Sun-frame speed uncertainty $`\sigma_{v_{\text{sun}}} = \mathrm{std}\negthinspace \left(\sqrt{\sum_{j} (v_{b,j}^{\text{SC}} + v_{\text{sc},j}^{\text{sun}})^{2}}\right)`$ is computed by the `uncertainties` package from the correlated fitted components plus the exact spacecraft-velocity offset — equivalent to the first-order Gaussian form $`\sqrt{\mathbf{g}_{\text{sun}}^{\top} \Sigma_{\mathbf{v}} \mathbf{g}_{\text{sun}}}`$ with $`\mathbf{g}_{\text{sun}} = \mathbf{v}_{b}^{\text{sun}}/\lvert\mathbf{v}_{b}^{\text{sun}}\rvert`$.
+
+## Alpha Particle Moments
+
+The alpha solar wind moments fitter (`calculate_alpha_solar_wind_moments.py`) reuses the proton forward model (`model_solar_wind_ideal_coincidence_rates`) and adds a 3-DOF least-squares fit over $`(n_{\alpha}, T_{\alpha}, \Delta v)`$ where alphas are constrained to drift along the local magnetic field:
+```math
+\mathbf{v}_{\alpha} = \mathbf{v}_{p}^{\ast} + \Delta v \thinspace  \hat{\mathbf{B}}.
+```
+This reflects the observed solar-wind tendency for non-field-aligned differential drift to be quenched by firehose/mirror instabilities.
+
+**Sign convention.** $`\Delta v > 0`$ means the alpha drifts along $`+\hat{\mathbf{B}}`$ relative to protons. Since MAG provides $`\mathbf{B}`$ with a physical polarity, $`\Delta v`$'s sign is interpretable.
+
+### Two-stage strategy
+
+When fitting alphas, we first fit the protons to each chunk as in the proton-only pipeline.
+We then use the proton model to obtain an initial guess and identify the alpha-peak step indices.
+Only those alpha-peak step indices are used in the fit.
+
+The combined proton+alpha model is
+```math
+C_{\text{obs}}(V) = \text{deadtime}\negthinspace \left(C_{p}^{\text{true}}(V; \theta_{p}^{\ast}) + C_{\alpha}^{\text{true}}(V; \theta_{\alpha})\right).
+```
+
+### Initial guess
+
+Stage 2's initial guess (`_alpha_initial_guess`) locates the alpha bump by subtracting the deadtime-corrected proton background from the sweep-averaged count rate:
+
+1. Reshape data into `(n_sweeps, n_bins)` via `_infer_sweep_layout`. Average the 5 sweeps: `count_avg`, `proton_bg_avg` (deadtime-corrected proton model per sweep, then averaged).
+2. Convert voltages to energies: $`E_{i} = k^* |V_{i}|`$ and form the residual $`\rho_{i} = \max(0,\thinspace  \overline{C_{i}} - 2\thinspace \overline{R_{i}^{p}})`$. The factor of 2 makes the alpha-bump finder less sensitive to the deep proton thermal tail leaking into the low-energy alpha bins.
+3. Call `get_alpha_peak_indices(residual, energies, proton_peak_index)` to return the alpha peak slice. The function walks from the proton peak toward higher energies (lower indices), past the gap where $`E_{i} < 1.5\thinspace E_{\text{proton-peak}}`$, until it finds a residual local minimum that bounds the alpha bump on the proton side; the high-energy side is bounded at $`E_{i} = 4\thinspace E_{\text{proton-peak}}`$.
+4. Require $`\geq 3`$ bins in the peak and at least one bin with positive residual. If either check fails, `FIT_ERROR` is set.
+5. Set $`T_{\alpha} = T_{p}`$. The alpha thermal width is fit by least squares in Stage 2; there is no Gaussian pre-fit on the residual.
+6. Density: compute a unit-density alpha forward model at $`\Delta v = 0`$ (using the proton bulk velocity as the alpha velocity seed), average across sweeps, and scale to match the mean residual at the peak:
+   ```math
+   n_{\alpha,0} = \max\negthinspace \left(\frac{\overline{\rho_{\text{peak}}}}{\overline{R_{\text{peak}}^{\alpha,\text{unit}}}},\thinspace  10^{-3}\right)
+   ```
+7. Return $`(n_{\alpha,0}, T_{\alpha}, \Delta v = 0, \text{peak\_bin\_indices})`$. The optimizer starts with $`\Delta v = 0`$. The returned `peak_bin_indices` are used to subset the residual axis for Stage 2 (see above).
+
+The figure below shows these steps on three real L2 spectra from `imap_swapi_l2_sci_20260101`. Top row: 5-sweep-averaged observed count rate (blue dots) vs the frozen proton model (orange), with the detected alpha peak region shaded green. Bottom row: residual (observed − proton model) at all bins (grey) and the peak bins (green circles).
+
+![Alpha peak-finding on real L2 spectra](figures/alpha_peak_finding.svg)
+
+*Generated by `docs/swapi/figure_src/plot_alpha_peak_finding.py`.*
+
+### Analytic Jacobian
+
+The optimizer uses an analytic 3-column Jacobian of the residuals in $`(\log n_{\alpha}, \log T_{\alpha}, \Delta v)`$ space, built using the same approach used by the proton fit, with two additional considerations:
+
+1. Because $`\mathbf{v}_{\alpha} = \mathbf{v}_{p}^{\ast} + \Delta v\thinspace \hat{\mathbf{B}}`$ with $`\mathbf{v}_{p}^{\ast}`$ held fixed,
+   ```math
+   \frac{\partial C_{\alpha}}{\partial (\Delta v)} = \nabla_{\mathbf{v}_{\alpha}} R_{\alpha} \cdot \hat{\mathbf{B}} = \sum_{c \in \{R, T, N\}} \frac{\partial C_{\alpha}}{\partial v_{\alpha, c}}\thinspace \hat{B}_{c}.
+   ```
+   The density and temperature columns are unchanged.
+
+2. Deadtime acts on the total coincidence rate $`C_{p} + C_{\alpha}`$, so the extra derivative factor is $\mathcal{D}^{2}(C_{p} + C_{\alpha})$ instead of $\mathcal{D}^{2}(C_{p})$.
+
+### Uncertainty propagation
+
+For alphas, we use the same approach as for the proton fit:
+
+```math
+\Sigma_{\text{stage 2}} = (J^{\top} J)^+ \thinspace  \bigl(J^{\top} W J\bigr) \thinspace  (J^{\top} J)^+ \quad\text{(3×3, in } (\log n_{\alpha}, \log T_{\alpha}, \Delta v) \text{ space)}, \qquad W_{ii} = \frac{r_{i}^{2}}{(1 - h_{ii})^{2}}
+```
+```math
+\sigma_{n_{\alpha}} = n_{\alpha} \sqrt{\Sigma_{\text{stage 2}}[0,0]}, \qquad \sigma_{T_{\alpha}} = T_{\alpha} \sqrt{\Sigma_{\text{stage 2}}[1,1]}, \qquad \sigma_{\Delta v} = \sqrt{\Sigma_{\text{stage 2}}[2,2]}
+```
+```math
+\Sigma_{\mathbf{v}_{\alpha}} = \Sigma_{\mathbf{v}_{p}} + \sigma_{\Delta v}^{2} \thinspace  \hat{\mathbf{B}}\hat{\mathbf{B}}^{\top}
+```
+
+This ignores the effect of proton-parameter uncertainty on Stage 2 residuals, so $`\sigma_{n_{\alpha}}, \sigma_{T_{\alpha}}`$ are lower bounds.
+
+### Bad fit detection
+
+For alphas, we use the $R^2 < 0.9$ check to set `BAD_FIT`.
+The points used to calculate $R^2$ are masked by the same mask used to fit the alphas.
+
+## Quality flags
+
+- `BAD_FIT` (= 2**2): fit converged but is flagged as an inaccurate description of the measurements.
+- `FIT_ERROR` (= 2**3): an error occurred, the fit failed to converge, or the initial guess stage failed.
+- `PRELIMINARY_MAG` (= 2**4, alpha-only): MAG L1D was used as the source for this run (L2 was unavailable).
+
+Gaps in the L2 data, ephemeris data, and MAG data (for alphas) result in fill values with no flag.
+
+For alphas, the proton flags are combined with the alpha quality flags.
+
+## References
+
+- Hayes, A. F., & Cai, L. (2007). Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation. *Behavior Research Methods*, 39(4), 709–722. https://doi.org/10.3758/BF03192961
+- Long, J. S., & Ervin, L. H. (2000). Using heteroscedasticity consistent standard errors in the linear regression model. *The American Statistician*, 54(3), 217–224. https://doi.org/10.1080/00031305.2000.10474549
+- MacKinnon, J. G., & White, H. (1985). Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. *Journal of Econometrics*, 29(3), 305–325. https://doi.org/10.1016/0304-4076(85)90158-7
+- Rankin, J. S., McComas, D. J., et al. (2025). Solar Wind and Pickup Ion (SWAPI) Instrument on NASA's Interstellar Mapping and Acceleration Probe (IMAP). *Space Science Reviews*, 221(8), 108. https://doi.org/10.1007/s11214-025-01229-8
+- Seber, G. A. F., & Wild, C. J. (1989). *Nonlinear Regression*. Wiley.
+- Stefanski, L. A., & Boos, D. D. (2002). The calculus of M-estimation. *The American Statistician*, 56(1), 29–38. https://doi.org/10.1198/000313002753631330
+- Tsoulfanidis, N. (1995). *Measurement and Detection of Radiation* (2nd ed.). Taylor & Francis.
+- White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. *Econometrica*, 48(4), 817–838. https://doi.org/10.2307/1912934
diff --git a/imap_l3_processing/cdf/config/imap_swapi_l3a_variable_attrs.yaml b/imap_l3_processing/cdf/config/imap_swapi_l3a_variable_attrs.yaml
index aefb9f653..d71a48749 100644
--- a/imap_l3_processing/cdf/config/imap_swapi_l3a_variable_attrs.yaml
+++ b/imap_l3_processing/cdf/config/imap_swapi_l3a_variable_attrs.yaml
@@ -279,6 +279,41 @@ proton_sw_speed_uncert:
   VALIDMAX: 10000
   FILLVAL: -1.000000e+31
   LABLAXIS: H+ SW speed uncertainty
+proton_sw_speed_sun:
+  NAME: proton_sw_speed_sun
+  DATA_TYPE: CDF_REAL4
+  CATDESC: Speed of proton solar wind in the sun's rest frame
+  DEPEND_0: epoch
+  VAR_TYPE: data
+  RECORD_VARYING: RV
+  DISPLAY_TYPE: time_series
+  VARIABLE_PURPOSE: PRIMARY_VAR,SUMMARY
+  FIELDNAM: Proton Solar Wind Speed Sun
+  FORMAT: F19.3
+  LABLAXIS: H+ SW speed sun
+  UNITS: km/s
+  VALIDMIN: 0
+  VALIDMAX: 10000
+  FILLVAL: -1.000000e+31
+  SCALETYP: linear
+  DELTA_PLUS_VAR: proton_sw_speed_sun_uncert
+  DELTA_MINUS_VAR: proton_sw_speed_sun_uncert
+  SCALEMIN: 0
+  SCALEMAX: 1000
+proton_sw_speed_sun_uncert:
+  NAME: proton_sw_speed_sun_uncert
+  DATA_TYPE: CDF_REAL4
+  CATDESC: Proton solar wind speed uncertainty in the sun's rest frame
+  DEPEND_0: epoch
+  VAR_TYPE: support_data
+  RECORD_VARYING: RV
+  FIELDNAM: Proton Solar Wind Speed Sun Uncertainty
+  FORMAT: F19.3
+  UNITS: km/s
+  VALIDMIN: 0
+  VALIDMAX: 10000
+  FILLVAL: -1.000000e+31
+  LABLAXIS: H+ SW speed sun uncertainty
 proton_sw_temperature:
   NAME: proton_sw_temperature
   DATA_TYPE: CDF_REAL4
@@ -349,76 +384,68 @@ proton_sw_density_uncert:
   VALIDMAX: 150
   FILLVAL: -1.000000e+31
   LABLAXIS: H+ SW density uncertainty
-proton_sw_clock_angle:
-  NAME: proton_sw_clock_angle
+proton_sw_bulk_velocity_rtn_sun:
+  NAME: proton_sw_bulk_velocity_rtn_sun
   DATA_TYPE: CDF_REAL4
-  CATDESC: Clock angle of proton solar wind
+  CATDESC: Proton solar wind bulk velocity in inertial RTN (sun's rest frame)
   DEPEND_0: epoch
+  DEPEND_1: rtn_label
   VAR_TYPE: data
   RECORD_VARYING: RV
   DISPLAY_TYPE: time_series
   VARIABLE_PURPOSE: PRIMARY_VAR,SUMMARY
-  FIELDNAM: Clock Angle Of Proton Solar Wind
+  FIELDNAM: Proton SW Bulk Velocity RTN Sun
   FORMAT: F19.3
-  LABLAXIS: Clock angle of H+ SW
-  UNITS: degrees
-  VALIDMIN: 0
-  VALIDMAX: 360
+  LABLAXIS: H+ SW bulk velocity RTN sun
+  UNITS: km/s
+  VALIDMIN: -2000
+  VALIDMAX: 2000
   FILLVAL: -1.000000e+31
-  SCALETYP: linear
-  DELTA_PLUS_VAR: proton_sw_clock_angle_uncert
-  DELTA_MINUS_VAR: proton_sw_clock_angle_uncert
-  SCALEMIN: 0
-  SCALEMAX: 360
-proton_sw_clock_angle_uncert:
-  NAME: proton_sw_clock_angle_uncert
+proton_sw_bulk_velocity_rtn_sun_covariance:
+  NAME: proton_sw_bulk_velocity_rtn_sun_covariance
   DATA_TYPE: CDF_REAL4
-  CATDESC: Proton solar wind clock angle uncertainty
+  CATDESC: Proton solar wind bulk velocity covariance in inertial RTN (sun's rest frame)
   DEPEND_0: epoch
   VAR_TYPE: support_data
   RECORD_VARYING: RV
-  FIELDNAM: Proton Solar Wind Clock Angle Uncertainty
+  FIELDNAM: Proton SW Bulk Velocity RTN Sun Covariance
   FORMAT: F19.3
-  UNITS: degrees
+  LABLAXIS: H+ SW bulk velocity RTN sun covariance
+  UNITS: km!E2!N/s!E2!N
   VALIDMIN: 0
-  VALIDMAX: 360
+  VALIDMAX: 1.0e+8
   FILLVAL: -1.000000e+31
-  LABLAXIS: H+ SW clock angle uncertainty
-proton_sw_deflection_angle:
-  NAME: proton_sw_deflection_angle
+proton_sw_bulk_velocity_rtn_sc:
+  NAME: proton_sw_bulk_velocity_rtn_sc
   DATA_TYPE: CDF_REAL4
-  CATDESC: Deflection angle of proton solar wind
+  CATDESC: Proton solar wind bulk velocity in RTN (spacecraft rest frame)
   DEPEND_0: epoch
+  DEPEND_1: rtn_label
   VAR_TYPE: data
   RECORD_VARYING: RV
   DISPLAY_TYPE: time_series
   VARIABLE_PURPOSE: PRIMARY_VAR,SUMMARY
-  FIELDNAM: Deflection Angle Of Proton Solar Wind
+  FIELDNAM: Proton SW Bulk Velocity RTN SC
   FORMAT: F19.3
-  LABLAXIS: H+ SW deflection angle
-  UNITS: degrees
-  VALIDMIN: 0
-  VALIDMAX: 180
+  LABLAXIS: H+ SW bulk velocity RTN SC
+  UNITS: km/s
+  VALIDMIN: -2000
+  VALIDMAX: 2000
   FILLVAL: -1.000000e+31
-  SCALETYP: linear
-  DELTA_PLUS_VAR: proton_sw_deflection_angle_uncert
-  DELTA_MINUS_VAR: proton_sw_deflection_angle_uncert
-  SCALEMIN: 0
-  SCALEMAX: 10
-proton_sw_deflection_angle_uncert:
-  NAME: proton_sw_deflection_angle_uncert
+proton_sw_bulk_velocity_rtn_sc_covariance:
+  NAME: proton_sw_bulk_velocity_rtn_sc_covariance
   DATA_TYPE: CDF_REAL4
-  CATDESC: Proton solar wind deflection angle uncertainty
+  CATDESC: Proton solar wind bulk velocity covariance in RTN (spacecraft rest frame)
   DEPEND_0: epoch
   VAR_TYPE: support_data
   RECORD_VARYING: RV
-  FIELDNAM: Proton Solar Wind Deflection Angle Uncertainty
+  FIELDNAM: Proton SW Bulk Velocity RTN SC Covariance
   FORMAT: F19.3
-  UNITS: degrees
+  LABLAXIS: H+ SW bulk velocity RTN SC covariance
+  UNITS: km!E2!N/s!E2!N
   VALIDMIN: 0
-  VALIDMAX: 360
+  VALIDMAX: 1.0e+8
   FILLVAL: -1.000000e+31
-  LABLAXIS: H+ SW deflection angle uncertainty
 alpha_sw_speed:
   NAME: alpha_sw_speed
   DATA_TYPE: CDF_REAL4
@@ -524,6 +551,85 @@ alpha_sw_density_uncert:
   VALIDMAX: 100
   FILLVAL: -1.000000e+31
   LABLAXIS: Alpha SW density uncertainty
+alpha_sw_velocity_rtn:
+  NAME: alpha_sw_velocity_rtn
+  DATA_TYPE: CDF_REAL4
+  CATDESC: Alpha solar wind bulk velocity in RTN
+  DEPEND_0: epoch
+  DEPEND_1: rtn_label
+  VAR_TYPE: data
+  RECORD_VARYING: RV
+  DISPLAY_TYPE: time_series
+  VARIABLE_PURPOSE: PRIMARY_VAR,SUMMARY
+  FIELDNAM: Alpha SW Bulk Velocity RTN
+  FORMAT: F19.3
+  LABLAXIS: Alpha SW bulk velocity RTN
+  UNITS: km/s
+  VALIDMIN: -2000
+  VALIDMAX: 2000
+  FILLVAL: -1.000000e+31
+alpha_sw_velocity_covariance_rtn:
+  NAME: alpha_sw_velocity_covariance_rtn
+  DATA_TYPE: CDF_REAL4
+  CATDESC: Alpha solar wind velocity covariance in RTN
+  DEPEND_0: epoch
+  VAR_TYPE: support_data
+  RECORD_VARYING: RV
+  FIELDNAM: Alpha SW Velocity Covariance RTN
+  FORMAT: F19.3
+  LABLAXIS: Alpha SW velocity covariance RTN
+  UNITS: km!E2!N/s!E2!N
+  VALIDMIN: 0
+  VALIDMAX: 1.0e+8
+  FILLVAL: -1.000000e+31
+alpha_sw_delta_v:
+  NAME: alpha_sw_delta_v
+  DATA_TYPE: CDF_REAL4
+  CATDESC: Alpha-proton differential streaming speed along B
+  DEPEND_0: epoch
+  VAR_TYPE: data
+  RECORD_VARYING: RV
+  DISPLAY_TYPE: time_series
+  VARIABLE_PURPOSE: PRIMARY_VAR,SUMMARY
+  FIELDNAM: Alpha SW Delta-V
+  FORMAT: F19.3
+  LABLAXIS: Alpha SW delta-v
+  UNITS: km/s
+  VALIDMIN: -2000
+  VALIDMAX: 2000
+  FILLVAL: -1.000000e+31
+  SCALETYP: linear
+  DELTA_PLUS_VAR: alpha_sw_delta_v_uncert
+  DELTA_MINUS_VAR: alpha_sw_delta_v_uncert
+alpha_sw_delta_v_uncert:
+  NAME: alpha_sw_delta_v_uncert
+  DATA_TYPE: CDF_REAL4
+  CATDESC: Alpha-proton differential streaming speed uncertainty
+  DEPEND_0: epoch
+  VAR_TYPE: support_data
+  RECORD_VARYING: RV
+  FIELDNAM: Alpha SW Delta-V Uncertainty
+  FORMAT: F19.3
+  LABLAXIS: Alpha SW delta-v uncertainty
+  UNITS: km/s
+  VALIDMIN: 0
+  VALIDMAX: 2000
+  FILLVAL: -1.000000e+31
+alpha_sw_b_hat_rtn:
+  NAME: alpha_sw_b_hat_rtn
+  DATA_TYPE: CDF_REAL4
+  CATDESC: Unit magnetic field vector in RTN used for alpha fit
+  DEPEND_0: epoch
+  DEPEND_1: rtn_label
+  VAR_TYPE: support_data
+  RECORD_VARYING: RV
+  FIELDNAM: Alpha SW B-hat RTN
+  FORMAT: F19.3
+  LABLAXIS: B-hat RTN
+  UNITS: ' '
+  VALIDMIN: -1
+  VALIDMAX: 1
+  FILLVAL: -1.000000e+31
 swp_flags:
   NAME: swp_flags
   DATA_TYPE: CDF_UINT2
@@ -577,4 +683,15 @@ alpha_sw_pre_lut_density:
   FILLVAL: -1.000000e+31
   SCALETYP: log
   SCALEMIN: 0.001
-  SCALEMAX: 5
\ No newline at end of file
+  SCALEMAX: 5
+
+rtn_label:
+  NAME: rtn_label
+  DATA_TYPE: CDF_CHAR
+  CATDESC: RTN component labels
+  VAR_TYPE: metadata
+  RECORD_VARYING: NRV
+  FIELDNAM: RTN Label
+  FORMAT: a1
+  FILLVAL: ' '
+
diff --git a/imap_l3_processing/cdf/data_product_definition_csv/imap_swapi_l3a_variable_attrs.csv b/imap_l3_processing/cdf/data_product_definition_csv/imap_swapi_l3a_variable_attrs.csv
index e70827857..027a04fcb 100644
--- a/imap_l3_processing/cdf/data_product_definition_csv/imap_swapi_l3a_variable_attrs.csv
+++ b/imap_l3_processing/cdf/data_product_definition_csv/imap_swapi_l3a_variable_attrs.csv
@@ -13,10 +13,6 @@ proton_sw_temperature,CDF_REAL4,Temperature of Proton Solar Wind,epoch,,,,data,R
 proton_sw_temperature_delta,CDF_REAL4,Proton Solar Wind Temperature Uncertainty,epoch,,,,support_data,RV,no_plot,,proton_sw_temperature_delta,F19.3,,K,0,9223372036854775807,-1.000000e+31,,,,,,,,,,,,,,,,,,,,,,,,
 proton_sw_density,CDF_REAL4,Density of Proton Solar Wind,epoch,,,,data,RV,time_series,"PRIMARY_VAR, SUMMARY",proton_sw_density,F19.3,Proton Solar Wind Density,cm!E-3!N,0,9223372036854775807,-1.000000e+31,,,,,linear,,,,,,,,,proton_sw_density_delta,proton_sw_density_delta,,,,0,16,,,,
 proton_sw_density_delta,CDF_REAL4,Proton Solar Wind Density Uncertainty,epoch,,,,support_data,RV,no_plot,,proton_sw_density_delta,F19.3,,cm!E-3!N,0,9223372036854775807,-1.000000e+31,,,,,,,,,,,,,,,,,,,,,,,,
-proton_sw_clock_angle,CDF_REAL4,Clock Angle of Proton Solar Wind,epoch,,,,data,RV,time_series,"PRIMARY_VAR, SUMMARY",proton_sw_clock_angle,F19.3,Proton Solar Wind Clock Angle,Degrees,0,9223372036854775807,-1.000000e+31,,,,,linear,,,,,,,,,proton_sw_clock_angle_delta,proton_sw_clock_angle_delta,,,,0,360,,,,
-proton_sw_clock_angle_delta,CDF_REAL4,Proton Solar Wind Clock Angle Uncertainty,epoch,,,,support_data,RV,no_plot,,proton_sw_clock_angle_delta,F19.3,,Degrees,0,9223372036854775807,-1.000000e+31,,,,,,,,,,,,,,,,,,,,,,,,
-proton_sw_deflection_angle,CDF_REAL4,Deflection Angle of Proton Solar Wind,epoch,,,,data,RV,time_series,"PRIMARY_VAR, SUMMARY",proton_sw_deflection_angle,F19.3,Proton Solar Wind Deflection Angle,Degrees,0,9223372036854775807,-1.000000e+31,,,,,linear,,,,,,,,,proton_sw_deflection_angle_delta,proton_sw_deflection_angle_delta,,,,0,360,,,,
-proton_sw_deflection_angle_delta,CDF_REAL4,Proton Solar Wind Deflection Angle Uncertainty,epoch,,,,support_data,RV,no_plot,,proton_sw_deflection_angle_delta,F19.3,,Degrees,0,9223372036854775807,-1.000000e+31,,,,,,,,,,,,,,,,,,,,,,,,
 alpha_sw_speed,CDF_REAL4,Speed of Alpha Solar Wind,epoch,,,,data,RV,time_series,"PRIMARY_VAR, SUMMARY",alpha_sw_speed,F19.3,Alpha Solar Wind Speed,km/s,0,9223372036854775807,-1.000000e+31,,,,,linear,,,,,,,,,alpha_sw_speed_delta,alpha_sw_speed_delta,,,,0,1000,,,,
 alpha_sw_speed_delta,CDF_REAL4,Alpha Solar Wind Speed Uncertainty,epoch,,,,support_data,RV,no_plot,,alpha_sw_speed_delta,F19.3,,km/s,0,9223372036854775807,-1.000000e+31,,,,,,,,,,,,,,,,,,,,,,,,
 alpha_sw_temperature,CDF_REAL4,Temperature of Alpha Solar Wind,epoch,,,,data,RV,time_series,"PRIMARY_VAR, SUMMARY",alpha_sw_temperature,F19.3,Alpha Solar Wind Temperature,K,0,9223372036854775807,-1.000000e+31,,,,,linear,,,,,,,,,alpha_sw_temperature_delta,alpha_sw_temperature_delta,,,,0,450000,,,,
diff --git a/imap_l3_processing/constants.py b/imap_l3_processing/constants.py
index 75d8b7c95..7a9335dc7 100644
--- a/imap_l3_processing/constants.py
+++ b/imap_l3_processing/constants.py
@@ -13,7 +13,14 @@
 PUI_PARTICLE_CHARGE_COULOMBS = PROTON_CHARGE_COULOMBS
 HE_PUI_PARTICLE_MASS_KG = 6.64556828337e-27
 
+PROTON_CHARGE_OVER_MASS_C_PER_KG = PROTON_CHARGE_COULOMBS / PROTON_MASS_KG
+ALPHA_CHARGE_OVER_MASS_C_PER_KG = ALPHA_PARTICLE_CHARGE_COULOMBS / ALPHA_PARTICLE_MASS_KG
+
+PROTON_MASS_PER_CHARGE_M_P_PER_E = 1.0
+ALPHA_MASS_PER_CHARGE_M_P_PER_E = (ALPHA_PARTICLE_MASS_KG / PROTON_MASS_KG) / (ALPHA_PARTICLE_CHARGE_COULOMBS / PROTON_CHARGE_COULOMBS)
+
 BOLTZMANN_CONSTANT_JOULES_PER_KELVIN = 1.380_649e-23
+EV_TO_KELVIN = PROTON_CHARGE_COULOMBS / BOLTZMANN_CONSTANT_JOULES_PER_KELVIN  # ≈ 11604.52 K/eV
 
 ONE_SECOND_IN_MICROSECONDS = 1_000_000
 ONE_SECOND_IN_NANOSECONDS = 1_000_000_000
@@ -44,6 +51,4 @@
 UNSIGNED_INT4_FILL_VALUE = 4294967295
 INT8_FILL_VALUE = -9223372036854775808
 
-SWAPI_EFFECTIVE_AREA_CM2 = 1.4053e-3
-
-TTJ2000_EPOCH = datetime.fromisoformat("2000-01-01T11:58:55.816")
\ No newline at end of file
+TTJ2000_EPOCH = datetime.fromisoformat("2000-01-01T11:58:55.816")
diff --git a/imap_l3_processing/swapi/constants.py b/imap_l3_processing/swapi/constants.py
new file mode 100644
index 000000000..9c21560ff
--- /dev/null
+++ b/imap_l3_processing/swapi/constants.py
@@ -0,0 +1,19 @@
+# Revised SWAPI ESA k-factor from high-resolution SIMION simulations (Q ≈ 1.89 eV/V at θ = 0).
+# Used internally by L3 for passband normalization and central-speed conversions.
+SWAPI_K_FACTOR = 1.89
+
+# Pre-launch k-factor used by the L2 product to label its `esa_energy` field as
+# `esa_energy = SWAPI_L2_K_FACTOR × |voltage|`. Different from SWAPI_K_FACTOR — divide L2's
+# `esa_energy` by this to recover true ESA voltage before any L3 processing.
+SWAPI_L2_K_FACTOR = 1.93
+
+# SWAPI ESA sweep bin layout (72 bins total, indices 0–71):
+#   Index 0       : always discarded (hardware artifact, never science data)
+#   Indices 1–62  : coarse sweep passbands (62 bins, uniform energy steps)
+#   Indices 63–71 : fine sweep passbands (9 bins, higher resolution near the proton peak)
+SWAPI_DISCARDED_BIN = 0
+SWAPI_COARSE_SWEEP_BINS = slice(1, 63)  # indices 1–62
+SWAPI_FINE_SWEEP_BINS = slice(63, 72)  # indices 63–71
+SWAPI_SCIENCE_BINS = slice(1, 72)  # indices 1–71, all usable bins (coarse + fine)
+
+SWAPI_LIVETIME_S = 0.145
diff --git a/imap_l3_processing/swapi/descriptors.py b/imap_l3_processing/swapi/descriptors.py
index 66c5db5c9..760e60c33 100644
--- a/imap_l3_processing/swapi/descriptors.py
+++ b/imap_l3_processing/swapi/descriptors.py
@@ -1,7 +1,5 @@
 SWAPI_L2_DESCRIPTOR = "sci"
-PROTON_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR = "proton-density-temperature-lut"
 ALPHA_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR = "alpha-density-temperature-lut"
-CLOCK_ANGLE_AND_FLOW_DEFLECTION_LOOKUP_TABLE_DESCRIPTOR = "clock-angle-and-flow-deflection-lut"
 GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR = "energy-gf-sw-lut"
 GEOMETRIC_FACTOR_PUI_LOOKUP_TABLE_DESCRIPTOR = "energy-gf-pui-lut"
 EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR = "efficiency-lut"
@@ -10,4 +8,10 @@
 HYDROGEN_INFLOW_VECTOR_DESCRIPTOR = "hydrogen-inflow-vector"
 HELIUM_INFLOW_VECTOR_DESCRIPTOR = "helium-inflow-vector"
 
+AZIMUTHAL_TRANSMISSION_DESCRIPTOR = "azimuthal-transmission"
+CENTRAL_EFFECTIVE_AREA_DESCRIPTOR = "central-effective-area"
+PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR = "passband-fit-coefficients"
+
 SWAPI_L3A_ALPHA_SW_DESCRIPTOR = "alpha-sw"
+
+MAG_RTN_DESCRIPTOR = "norm-rtn"
diff --git a/imap_l3_processing/swapi/l3a/chunk_fits.py b/imap_l3_processing/swapi/l3a/chunk_fits.py
new file mode 100644
index 000000000..6dc103812
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/chunk_fits.py
@@ -0,0 +1,421 @@
+import logging
+import multiprocessing
+import os
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any
+
+import numpy as np
+from spacepy import pycdf
+from uncertainties import ufloat
+
+from imap_l3_processing.constants import (
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    THIRTY_SECONDS_IN_NANOSECONDS,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.alpha.calculate_alpha_solar_wind_moments import (
+    AlphaSolarWindMoments,
+    fit_solar_wind_alpha_moments,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_model import (
+    ProtonSolarWindFitResult,
+    fit_solar_wind_proton_model,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.initial_guess import (
+    esa_voltage_to_proton_speed,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    build_solar_wind_fit_context,
+)
+from imap_l3_processing.swapi.constants import (
+    SWAPI_COARSE_SWEEP_BINS,
+    SWAPI_L2_K_FACTOR,
+    SWAPI_SCIENCE_BINS,
+)
+from imap_l3_processing.swapi.l3a.utils import (
+    chunk_epoch,
+    compute_direction_of_mean_magnetic_field_over_chunk,
+    get_spacecraft_velocity_rtn,
+    get_swapi_geometry,
+    measurement_times,
+)
+from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
+
+logger = logging.getLogger(__name__)
+
+_shared: dict[str, Any] = {}
+
+
+class ChunkFitter(ABC):
+    @abstractmethod
+    def precompute_geometry(self, chunk) -> tuple: ...
+
+    @abstractmethod
+    def fit_chunk(self, chunk, *geometry) -> dict[str, Any]: ...
+
+
+class ParallelChunkRunner:
+    def __init__(self, swapi_response, efficiency_table):
+        self._swapi_response = swapi_response
+        self._efficiency_table = efficiency_table
+
+    def run(self, chunks, fitter: ChunkFitter) -> dict[str, np.ndarray]:
+        geometries = [fitter.precompute_geometry(chunk) for chunk in chunks]
+
+        with multiprocessing.get_context("fork").Pool(
+            processes=os.cpu_count(),
+            initializer=_init_worker,
+            initargs=(self._swapi_response, self._efficiency_table, fitter),
+        ) as pool:
+            results = pool.starmap(
+                _run_one, [(chunk, geom) for chunk, geom in zip(chunks, geometries)]
+            )
+
+        return {k: np.array([r[k] for r in results]) for k in results[0].keys()}
+
+
+def _init_worker(swapi_response, efficiency_table, fitter):
+    _shared["swapi_response"] = swapi_response
+    _shared["efficiency_table"] = efficiency_table
+    _shared["fitter"] = fitter
+
+
+def _run_one(chunk, geom):
+    return _shared["fitter"].fit_chunk(chunk, *geom)
+
+
+class ProtonChunkFitter(ChunkFitter):
+    def precompute_geometry(self, chunk):
+        epoch = chunk_epoch(chunk)
+        rm = None
+        sc_vel = None
+        try:
+            rm = get_swapi_geometry(measurement_times(chunk, SWAPI_SCIENCE_BINS))
+        except Exception:
+            logger.warning(
+                "SPICE gap in rotation matrices.",
+                exc_info=True,
+            )
+
+        if rm is not None:
+            try:
+                sc_vel = get_spacecraft_velocity_rtn(epoch)
+            except Exception:
+                logger.warning(
+                    "SPICE gap in spacecraft velocity.",
+                    exc_info=True,
+                )
+
+        return (epoch, rm, sc_vel)
+
+    def fit_chunk(
+        self,
+        data_chunk,
+        epoch,
+        rotation_matrices,
+        sc_velocity_rtn,
+    ):
+        result = _fit_proton(data_chunk, epoch, rotation_matrices)
+        return _proton_moments_from_fit(result, epoch, data_chunk, sc_velocity_rtn)
+
+
+class AlphaChunkFitter(ChunkFitter):
+    def __init__(self, mag_data):
+        self.mag_data = mag_data
+
+    def precompute_geometry(self, chunk):
+        epoch = chunk_epoch(chunk)
+        try:
+            rm = get_swapi_geometry(measurement_times(chunk, SWAPI_SCIENCE_BINS))
+        except Exception:
+            logger.info(
+                f"Missing SPICE information at epoch {pycdf.lib.tt2000_to_datetime(int(epoch))}, continuing with fill value"
+            )
+            rm = None
+        b_hat = compute_direction_of_mean_magnetic_field_over_chunk(
+            self.mag_data, int(epoch), int(THIRTY_SECONDS_IN_NANOSECONDS)
+        )
+        return (epoch, rm, b_hat)
+
+    def fit_chunk(
+        self,
+        data_chunk,
+        epoch,
+        rotation_matrices,
+        magnetic_field_direction,
+    ):
+        result = _fit_alpha(
+            data_chunk, epoch, rotation_matrices, magnetic_field_direction
+        )
+        return _alpha_moments_from_fit(result, epoch)
+
+
+def _proton_moments_from_fit(result, epoch, data_chunk, sc_velocity_rtn):
+    speed = sum(component**2 for component in result.bulk_velocity_rtn) ** 0.5
+    speed_nom, speed_unc = speed.nominal_value, speed.std_dev
+    bulk_velocity_rtn_sc = result.bulk_velocity_rtn_nominal()
+    velocity_covariance_sc = result.bulk_velocity_rtn_covariance()
+    density_nom, density_unc = result.density.nominal_value, result.density.std_dev
+    temp_nom, temp_unc = result.temperature.nominal_value, result.temperature.std_dev
+
+    if sc_velocity_rtn is not None:
+        bulk_velocity_rtn_sun = bulk_velocity_rtn_sc + sc_velocity_rtn
+        velocity_covariance_sun = velocity_covariance_sc
+        sun_velocity_unc = [
+            component + sc_component
+            for component, sc_component in zip(
+                result.bulk_velocity_rtn, sc_velocity_rtn
+            )
+        ]
+        sun_speed = sum(component**2 for component in sun_velocity_unc) ** 0.5
+        sun_speed_nom, sun_speed_unc = sun_speed.nominal_value, sun_speed.std_dev
+    else:
+        logger.warning(
+            f"Proton fit at epoch {pycdf.lib.tt2000_to_datetime(int(epoch))}: missing spacecraft velocity; sun-frame outputs are fill values"
+        )
+        bulk_velocity_rtn_sun = np.full(3, np.nan)
+        velocity_covariance_sun = np.full((3, 3), np.nan)
+        sun_speed_nom = sun_speed_unc = np.nan
+
+    if not np.isfinite(speed_nom):
+        speed_nom = _peak_proton_speed_kms(data_chunk)
+
+    return dict(
+        epoch=epoch,
+        proton_sw_speed=speed_nom,
+        proton_sw_speed_uncert=speed_unc,
+        proton_sw_speed_sun=sun_speed_nom,
+        proton_sw_speed_sun_uncert=sun_speed_unc,
+        proton_sw_temperature=temp_nom,
+        proton_sw_temperature_uncert=temp_unc,
+        proton_sw_density=density_nom,
+        proton_sw_density_uncert=density_unc,
+        proton_sw_bulk_velocity_rtn_sun=bulk_velocity_rtn_sun,
+        proton_sw_bulk_velocity_rtn_sun_covariance=velocity_covariance_sun,
+        proton_sw_bulk_velocity_rtn_sc=bulk_velocity_rtn_sc,
+        proton_sw_bulk_velocity_rtn_sc_covariance=velocity_covariance_sc,
+        quality_flags=result.bad_fit_flag,
+    )
+
+
+def _alpha_moments_from_fit(result, epoch):
+    alpha = result.alpha_moments
+    return dict(
+        epoch=epoch,
+        alpha_sw_density=alpha.density.nominal_value,
+        alpha_sw_density_uncert=alpha.density.std_dev,
+        alpha_sw_temperature=alpha.temperature.nominal_value,
+        alpha_sw_temperature_uncert=alpha.temperature.std_dev,
+        alpha_sw_velocity_rtn=alpha.bulk_velocity_rtn_nominal(),
+        alpha_sw_velocity_covariance_rtn=alpha.bulk_velocity_rtn_covariance(),
+        alpha_sw_delta_v=alpha.delta_v.nominal_value,
+        alpha_sw_delta_v_uncert=alpha.delta_v.std_dev,
+        alpha_sw_b_hat_rtn=result.b_hat_rtn,
+        quality_flags=result.bad_fit_flag,
+    )
+
+
+_PEAK_SPEED_HALF_WIDTH = 4
+
+
+def _peak_proton_speed_kms(data_chunk) -> float:
+    count_rate = data_chunk.coincidence_count_rate[:, SWAPI_COARSE_SWEEP_BINS]
+    voltage = data_chunk.energy[:, SWAPI_COARSE_SWEEP_BINS] / SWAPI_L2_K_FACTOR
+    mean_count_rate = np.nanmean(count_rate, axis=0)
+    mean_voltage = np.nanmean(voltage, axis=0)
+    if not np.any(np.isfinite(mean_count_rate)):
+        return np.nan
+    peak_index = int(np.nanargmax(mean_count_rate))
+    n_bins = mean_count_rate.shape[0]
+    clamped_peak_index = max(
+        _PEAK_SPEED_HALF_WIDTH, min(peak_index, n_bins - _PEAK_SPEED_HALF_WIDTH - 1)
+    )
+    window = slice(
+        clamped_peak_index - _PEAK_SPEED_HALF_WIDTH,
+        clamped_peak_index + _PEAK_SPEED_HALF_WIDTH + 1,
+    )
+    rate_window = mean_count_rate[window]
+    voltage_window = mean_voltage[window]
+    valid = (
+        np.isfinite(rate_window)
+        & np.isfinite(voltage_window)
+        & (voltage_window > 0.0)
+        & (rate_window >= 0.0)
+    )
+    if not np.any(valid):
+        return np.nan
+    speed = esa_voltage_to_proton_speed(voltage_window[valid])
+    weights = rate_window[valid]
+    weight_sum = float(np.sum(weights))
+    if not np.isfinite(weight_sum) or weight_sum <= 0.0:
+        return np.nan
+    return float(np.sum(weights * speed) / weight_sum)
+
+
+def _eff_scale(efficiency_table, epoch, kind):
+    eps_lab = float(efficiency_table.eps_p_lab)
+    if kind == "proton":
+        return float(efficiency_table.get_proton_efficiency_for(epoch)) / eps_lab
+    return float(efficiency_table.get_alpha_efficiency_for(epoch)) / eps_lab
+
+
+def _coarse_subset_of_science_rotations(
+    science_rotation_matrices: np.ndarray, n_sweeps: int
+) -> np.ndarray:
+    """Slice per-sweep coarse-bin rotations out of a flat science-bin rotation
+    array. Coarse bins are the first 62 of the 71 science bins per sweep."""
+    n_science_bins = SWAPI_SCIENCE_BINS.stop - SWAPI_SCIENCE_BINS.start
+    n_coarse_bins = SWAPI_COARSE_SWEEP_BINS.stop - SWAPI_COARSE_SWEEP_BINS.start
+    return (
+        science_rotation_matrices.reshape(n_sweeps, n_science_bins, 3, 3)[
+            :, :n_coarse_bins
+        ]
+        .reshape(-1, 3, 3)
+    )
+
+
+def _fit_proton(
+    data_chunk, epoch, rotation_matrices
+) -> ProtonSolarWindFitResult:
+    if rotation_matrices is None:
+        logger.warning(
+            f"Proton fit at epoch {pycdf.lib.tt2000_to_datetime(int(epoch))}: missing rotation matrices; using fill values"
+        )
+        return _nan_proton_result(SwapiL3Flags.NONE)
+    if np.any(np.isnan(data_chunk.coincidence_count_rate[:, SWAPI_SCIENCE_BINS])):
+        logger.warning(
+            f"Proton fit at epoch {pycdf.lib.tt2000_to_datetime(int(epoch))}: NaN in input count rate; using fill values"
+        )
+        return _nan_proton_result(SwapiL3Flags.NONE)
+    swapi_response = _shared["swapi_response"]
+    efficiency_table = _shared["efficiency_table"]
+    count_rates = data_chunk.coincidence_count_rate[:, SWAPI_SCIENCE_BINS]
+    voltages = data_chunk.energy[:, SWAPI_SCIENCE_BINS] / SWAPI_L2_K_FACTOR
+    ctx = build_solar_wind_fit_context(
+        count_rate=count_rates,
+        esa_voltage=voltages,
+        swapi_response=swapi_response,
+        central_effective_area_scale=_eff_scale(efficiency_table, epoch, "proton"),
+        rotation_matrices=rotation_matrices,
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    )
+    try:
+        result = fit_solar_wind_proton_model(ctx)
+    except Exception:
+        logger.warning(
+            f"Proton fit at epoch {pycdf.lib.tt2000_to_datetime(int(epoch))}: exception during fit; using fill values",
+            exc_info=True,
+        )
+        return _nan_proton_result(SwapiL3Flags.FIT_ERROR)
+
+    return result
+
+
+def _nan_proton_result(flag) -> ProtonSolarWindFitResult:
+    nan = ufloat(np.nan, np.nan)
+    return ProtonSolarWindFitResult(
+        density=nan,
+        temperature=nan,
+        bulk_velocity_rtn=(nan, nan, nan),
+        bad_fit_flag=int(flag),
+    )
+
+
+@dataclass
+class AlphaSolarWindFitResult:
+    """Chunk-level alpha output: alpha moments + the proton moments they
+    reference + the B̂ used as the field-aligned drift constraint, plus the
+    consolidated `bad_fit_flag` rolled up from every stage that could fail
+    (missing B̂, proton fit, alpha LM)."""
+
+    alpha_moments: AlphaSolarWindMoments
+    proton_moments: ProtonSolarWindFitResult
+    b_hat_rtn: np.ndarray
+    bad_fit_flag: int
+
+
+def _nan_alpha_moments(flag) -> AlphaSolarWindMoments:
+    nan = ufloat(np.nan, np.nan)
+    return AlphaSolarWindMoments(
+        density=nan,
+        temperature=nan,
+        bulk_velocity_rtn=(nan, nan, nan),
+        delta_v=nan,
+        bad_fit_flag=int(flag),
+    )
+
+
+def _fit_alpha(
+    data_chunk,
+    epoch,
+    rotation_matrices,
+    magnetic_field_direction,
+) -> AlphaSolarWindFitResult:
+    nan_b_hat = np.full(3, np.nan)
+    if (
+        magnetic_field_direction is None
+        or not np.all(np.isfinite(magnetic_field_direction))
+    ):
+        logger.warning(
+            f"Alpha fit at epoch {pycdf.lib.tt2000_to_datetime(int(epoch))}: missing or non-finite magnetic field direction; using fill values"
+        )
+        return AlphaSolarWindFitResult(
+            alpha_moments=_nan_alpha_moments(SwapiL3Flags.NONE),
+            proton_moments=_nan_proton_result(SwapiL3Flags.NONE),
+            b_hat_rtn=nan_b_hat,
+            bad_fit_flag=int(SwapiL3Flags.NONE),
+        )
+
+    proton_moments = _fit_proton(data_chunk, epoch, rotation_matrices)
+    if not np.all(
+        np.isfinite(
+            [component.nominal_value for component in proton_moments.bulk_velocity_rtn]
+        )
+    ):
+        return AlphaSolarWindFitResult(
+            alpha_moments=_nan_alpha_moments(proton_moments.bad_fit_flag),
+            proton_moments=proton_moments,
+            b_hat_rtn=nan_b_hat,
+            bad_fit_flag=int(proton_moments.bad_fit_flag),
+        )
+
+    swapi_response = _shared["swapi_response"]
+    efficiency_table = _shared["efficiency_table"]
+    try:
+        count_rates = data_chunk.coincidence_count_rate[:, SWAPI_COARSE_SWEEP_BINS]
+        voltages = data_chunk.energy[:, SWAPI_COARSE_SWEEP_BINS] / SWAPI_L2_K_FACTOR
+        times = measurement_times(data_chunk, SWAPI_COARSE_SWEEP_BINS)
+        coarse_rotation_matrices = _coarse_subset_of_science_rotations(
+            rotation_matrices, n_sweeps=data_chunk.sci_start_time.shape[0]
+        )
+        alpha_moments = fit_solar_wind_alpha_moments(
+            count_rates,
+            voltages,
+            times,
+            swapi_response,
+            proton_moments,
+            magnetic_field_direction,
+            _eff_scale(efficiency_table, epoch, "alpha"),
+            _eff_scale(efficiency_table, epoch, "proton"),
+            rotation_matrices=coarse_rotation_matrices,
+        )
+    except Exception:
+        logger.warning(
+            f"Alpha fit at epoch {pycdf.lib.tt2000_to_datetime(int(epoch))}: exception during fit; using fill values",
+            exc_info=True,
+        )
+        return AlphaSolarWindFitResult(
+            alpha_moments=_nan_alpha_moments(SwapiL3Flags.FIT_ERROR),
+            proton_moments=proton_moments,
+            b_hat_rtn=nan_b_hat,
+            bad_fit_flag=int(SwapiL3Flags.FIT_ERROR),
+        )
+
+    return AlphaSolarWindFitResult(
+        alpha_moments=alpha_moments,
+        proton_moments=proton_moments,
+        b_hat_rtn=magnetic_field_direction,
+        bad_fit_flag=int(alpha_moments.bad_fit_flag),
+    )
diff --git a/imap_l3_processing/swapi/l3a/models.py b/imap_l3_processing/swapi/l3a/models.py
index d53ce3766..373be1c43 100644
--- a/imap_l3_processing/swapi/l3a/models.py
+++ b/imap_l3_processing/swapi/l3a/models.py
@@ -4,7 +4,10 @@
 import numpy as np
 from uncertainties.unumpy import nominal_values, std_devs
 
-from imap_l3_processing.constants import THIRTY_SECONDS_IN_NANOSECONDS, FIVE_MINUTES_IN_NANOSECONDS
+from imap_l3_processing.constants import (
+    THIRTY_SECONDS_IN_NANOSECONDS,
+    FIVE_MINUTES_IN_NANOSECONDS,
+)
 from imap_l3_processing.models import DataProduct, DataProductVariable
 from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
 
@@ -12,25 +15,33 @@
 EPOCH_DELTA_CDF_VAR_NAME = "epoch_delta"
 PROTON_SOLAR_WIND_SPEED_CDF_VAR_NAME = "proton_sw_speed"
 PROTON_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME = "proton_sw_speed_uncert"
+PROTON_SOLAR_WIND_SPEED_SUN_CDF_VAR_NAME = "proton_sw_speed_sun"
+PROTON_SOLAR_WIND_SPEED_SUN_UNCERTAINTY_CDF_VAR_NAME = "proton_sw_speed_sun_uncert"
 PROTON_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME = "proton_sw_temperature"
 PROTON_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME = "proton_sw_temperature_uncert"
 PROTON_SOLAR_WIND_DENSITY_CDF_VAR_NAME = "proton_sw_density"
 PROTON_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME = "proton_sw_density_uncert"
 
-PROTON_SOLAR_WIND_CLOCK_ANGLE_CDF_VAR_NAME = "proton_sw_clock_angle"
-PROTON_SOLAR_WIND_CLOCK_ANGLE_UNCERTAINTY_CDF_VAR_NAME = "proton_sw_clock_angle_uncert"
+PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SUN_CDF_VAR_NAME = "proton_sw_bulk_velocity_rtn_sun"
+PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SUN_COVARIANCE_CDF_VAR_NAME = (
+    "proton_sw_bulk_velocity_rtn_sun_covariance"
+)
+PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SC_CDF_VAR_NAME = "proton_sw_bulk_velocity_rtn_sc"
+PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SC_COVARIANCE_CDF_VAR_NAME = (
+    "proton_sw_bulk_velocity_rtn_sc_covariance"
+)
 
-PROTON_SOLAR_WIND_DEFLECTION_ANGLE_CDF_VAR_NAME = "proton_sw_deflection_angle"
-PROTON_SOLAR_WIND_DEFLECTION_ANGLE_UNCERTAINTY_CDF_VAR_NAME = "proton_sw_deflection_angle_uncert"
-
-ALPHA_SOLAR_WIND_SPEED_CDF_VAR_NAME = "alpha_sw_speed"
-ALPHA_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME = "alpha_sw_speed_uncert"
 ALPHA_SOLAR_WIND_DENSITY_CDF_VAR_NAME = "alpha_sw_density"
 ALPHA_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME = "alpha_sw_density_uncert"
 ALPHA_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME = "alpha_sw_temperature"
 ALPHA_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME = "alpha_sw_temperature_uncert"
-ALPHA_SOLAR_WIND_PRE_LUT_DENSITY_CDF_VAR_NAME = "alpha_sw_pre_lut_density"
-ALPHA_SOLAR_WIND_PRE_LUT_TEMPERATURE_CDF_VAR_NAME = "alpha_sw_pre_lut_temperature"
+ALPHA_SOLAR_WIND_VELOCITY_RTN_CDF_VAR_NAME = "alpha_sw_velocity_rtn"
+ALPHA_SOLAR_WIND_VELOCITY_COVARIANCE_RTN_CDF_VAR_NAME = (
+    "alpha_sw_velocity_covariance_rtn"
+)
+ALPHA_SOLAR_WIND_DELTA_V_CDF_VAR_NAME = "alpha_sw_delta_v"
+ALPHA_SOLAR_WIND_DELTA_V_UNCERT_CDF_VAR_NAME = "alpha_sw_delta_v_uncert"
+ALPHA_SOLAR_WIND_B_HAT_RTN_CDF_VAR_NAME = "alpha_sw_b_hat_rtn"
 
 PUI_COOLING_INDEX_CDF_VAR_NAME = "pui_cooling_index"
 PUI_IONIZATION_RATE_CDF_VAR_NAME = "pui_ionization_rate"
@@ -50,61 +61,130 @@
 
 @dataclass
 class SwapiL3ProtonSolarWindData(DataProduct):
-    epoch: np.ndarray[float]
-    proton_sw_speed: np.ndarray[float]
-    proton_sw_temperature: np.ndarray[float]
-    proton_sw_density: np.ndarray[float]
-    proton_sw_clock_angle: np.ndarray[float]
-    proton_sw_deflection_angle: np.ndarray[float]
+    epoch: np.ndarray
+    proton_sw_speed: np.ndarray
+    proton_sw_speed_uncert: np.ndarray
+    proton_sw_speed_sun: np.ndarray
+    proton_sw_speed_sun_uncert: np.ndarray
+    proton_sw_temperature: np.ndarray
+    proton_sw_temperature_uncert: np.ndarray
+    proton_sw_density: np.ndarray
+    proton_sw_density_uncert: np.ndarray
+    proton_sw_bulk_velocity_rtn_sun: np.ndarray  # shape (N, 3), km/s, inertial RTN
+    proton_sw_bulk_velocity_rtn_sun_covariance: np.ndarray  # shape (N, 3, 3), km²/s²
+    proton_sw_bulk_velocity_rtn_sc: np.ndarray  # shape (N, 3), km/s, RTN in SC rest frame
+    proton_sw_bulk_velocity_rtn_sc_covariance: np.ndarray  # shape (N, 3, 3), km²/s²
     quality_flags: np.ndarray[SwapiL3Flags]
 
     def to_data_product_variables(self) -> list[DataProductVariable]:
         return [
             DataProductVariable(EPOCH_CDF_VAR_NAME, self.epoch),
-            DataProductVariable(PROTON_SOLAR_WIND_SPEED_CDF_VAR_NAME, nominal_values(self.proton_sw_speed)),
-            DataProductVariable(PROTON_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME, std_devs(self.proton_sw_speed)),
-            DataProductVariable(EPOCH_DELTA_CDF_VAR_NAME, np.full_like(self.epoch, THIRTY_SECONDS_IN_NANOSECONDS)),
-            DataProductVariable(PROTON_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME, nominal_values(self.proton_sw_temperature)),
-            DataProductVariable(PROTON_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME,
-                                std_devs(self.proton_sw_temperature)),
-            DataProductVariable(PROTON_SOLAR_WIND_DENSITY_CDF_VAR_NAME, nominal_values(self.proton_sw_density)),
-            DataProductVariable(PROTON_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME,
-                                std_devs(self.proton_sw_density)),
-            DataProductVariable(PROTON_SOLAR_WIND_CLOCK_ANGLE_CDF_VAR_NAME, nominal_values(self.proton_sw_clock_angle)),
-            DataProductVariable(PROTON_SOLAR_WIND_CLOCK_ANGLE_UNCERTAINTY_CDF_VAR_NAME,
-                                std_devs(self.proton_sw_clock_angle)),
-            DataProductVariable(PROTON_SOLAR_WIND_DEFLECTION_ANGLE_CDF_VAR_NAME,
-                                nominal_values(self.proton_sw_deflection_angle)),
-            DataProductVariable(PROTON_SOLAR_WIND_DEFLECTION_ANGLE_UNCERTAINTY_CDF_VAR_NAME,
-                                std_devs(self.proton_sw_deflection_angle)),
-            DataProductVariable(SWAPI_QUALITY_FLAGS_CDF_VAR_NAME, self.quality_flags)
+            DataProductVariable(
+                PROTON_SOLAR_WIND_SPEED_CDF_VAR_NAME, self.proton_sw_speed
+            ),
+            DataProductVariable(
+                PROTON_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME,
+                self.proton_sw_speed_uncert,
+            ),
+            DataProductVariable(
+                PROTON_SOLAR_WIND_SPEED_SUN_CDF_VAR_NAME,
+                self.proton_sw_speed_sun,
+            ),
+            DataProductVariable(
+                PROTON_SOLAR_WIND_SPEED_SUN_UNCERTAINTY_CDF_VAR_NAME,
+                self.proton_sw_speed_sun_uncert,
+            ),
+            DataProductVariable(
+                EPOCH_DELTA_CDF_VAR_NAME,
+                np.full_like(self.epoch, THIRTY_SECONDS_IN_NANOSECONDS),
+            ),
+            DataProductVariable(
+                PROTON_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME, self.proton_sw_temperature
+            ),
+            DataProductVariable(
+                PROTON_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME,
+                self.proton_sw_temperature_uncert,
+            ),
+            DataProductVariable(
+                PROTON_SOLAR_WIND_DENSITY_CDF_VAR_NAME, self.proton_sw_density
+            ),
+            DataProductVariable(
+                PROTON_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME,
+                self.proton_sw_density_uncert,
+            ),
+            DataProductVariable(
+                PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SUN_CDF_VAR_NAME,
+                self.proton_sw_bulk_velocity_rtn_sun,
+            ),
+            DataProductVariable(
+                PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SUN_COVARIANCE_CDF_VAR_NAME,
+                self.proton_sw_bulk_velocity_rtn_sun_covariance,
+            ),
+            DataProductVariable(
+                PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SC_CDF_VAR_NAME,
+                self.proton_sw_bulk_velocity_rtn_sc,
+            ),
+            DataProductVariable(
+                PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SC_COVARIANCE_CDF_VAR_NAME,
+                self.proton_sw_bulk_velocity_rtn_sc_covariance,
+            ),
+            DataProductVariable(SWAPI_QUALITY_FLAGS_CDF_VAR_NAME, self.quality_flags),
         ]
 
 
 @dataclass
 class SwapiL3AlphaSolarWindData(DataProduct):
     epoch: np.ndarray[datetime]
-    alpha_sw_speed: np.ndarray
-    alpha_sw_temperature: np.ndarray
     alpha_sw_density: np.ndarray
-    bad_fit_flag: np.ndarray
-    alpha_sw_pre_lut_temperature: np.ndarray
-    alpha_sw_pre_lut_density: np.ndarray
+    alpha_sw_density_uncert: np.ndarray
+    alpha_sw_temperature: np.ndarray
+    alpha_sw_temperature_uncert: np.ndarray
+    alpha_sw_velocity_rtn: np.ndarray
+    alpha_sw_velocity_covariance_rtn: np.ndarray
+    alpha_sw_delta_v: np.ndarray
+    alpha_sw_delta_v_uncert: np.ndarray
+    alpha_sw_b_hat_rtn: np.ndarray
+    quality_flags: np.ndarray
 
     def to_data_product_variables(self) -> list[DataProductVariable]:
         return [
             DataProductVariable(EPOCH_CDF_VAR_NAME, self.epoch),
-            DataProductVariable(EPOCH_DELTA_CDF_VAR_NAME, np.full_like(self.epoch, THIRTY_SECONDS_IN_NANOSECONDS)),
-            DataProductVariable(ALPHA_SOLAR_WIND_SPEED_CDF_VAR_NAME, nominal_values(self.alpha_sw_speed)),
-            DataProductVariable(ALPHA_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME, std_devs(self.alpha_sw_speed)),
-            DataProductVariable(ALPHA_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME, nominal_values(self.alpha_sw_temperature)),
-            DataProductVariable(ALPHA_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME,
-                                std_devs(self.alpha_sw_temperature)),
-            DataProductVariable(ALPHA_SOLAR_WIND_DENSITY_CDF_VAR_NAME, nominal_values(self.alpha_sw_density)),
-            DataProductVariable(ALPHA_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME, std_devs(self.alpha_sw_density)),
-            DataProductVariable(SWAPI_QUALITY_FLAGS_CDF_VAR_NAME, self.bad_fit_flag),
-            DataProductVariable(ALPHA_SOLAR_WIND_PRE_LUT_TEMPERATURE_CDF_VAR_NAME, nominal_values(self.alpha_sw_pre_lut_temperature)),
-            DataProductVariable(ALPHA_SOLAR_WIND_PRE_LUT_DENSITY_CDF_VAR_NAME, nominal_values(self.alpha_sw_pre_lut_density)),
+            DataProductVariable(
+                EPOCH_DELTA_CDF_VAR_NAME,
+                np.full_like(self.epoch, THIRTY_SECONDS_IN_NANOSECONDS),
+            ),
+            DataProductVariable(
+                ALPHA_SOLAR_WIND_DENSITY_CDF_VAR_NAME, self.alpha_sw_density
+            ),
+            DataProductVariable(
+                ALPHA_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME,
+                self.alpha_sw_density_uncert,
+            ),
+            DataProductVariable(
+                ALPHA_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME, self.alpha_sw_temperature
+            ),
+            DataProductVariable(
+                ALPHA_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME,
+                self.alpha_sw_temperature_uncert,
+            ),
+            DataProductVariable(
+                ALPHA_SOLAR_WIND_VELOCITY_RTN_CDF_VAR_NAME, self.alpha_sw_velocity_rtn
+            ),
+            DataProductVariable(
+                ALPHA_SOLAR_WIND_VELOCITY_COVARIANCE_RTN_CDF_VAR_NAME,
+                self.alpha_sw_velocity_covariance_rtn,
+            ),
+            DataProductVariable(
+                ALPHA_SOLAR_WIND_DELTA_V_CDF_VAR_NAME, self.alpha_sw_delta_v
+            ),
+            DataProductVariable(
+                ALPHA_SOLAR_WIND_DELTA_V_UNCERT_CDF_VAR_NAME,
+                self.alpha_sw_delta_v_uncert,
+            ),
+            DataProductVariable(
+                ALPHA_SOLAR_WIND_B_HAT_RTN_CDF_VAR_NAME, self.alpha_sw_b_hat_rtn
+            ),
+            DataProductVariable(SWAPI_QUALITY_FLAGS_CDF_VAR_NAME, self.quality_flags),
         ]
 
 
@@ -122,19 +202,47 @@ class SwapiL3PickupIonData(DataProduct):
     def to_data_product_variables(self) -> list[DataProductVariable]:
         return [
             DataProductVariable(EPOCH_CDF_VAR_NAME, self.epoch),
-            DataProductVariable(EPOCH_DELTA_CDF_VAR_NAME, np.full_like(self.epoch, FIVE_MINUTES_IN_NANOSECONDS)),
-            DataProductVariable(PUI_COOLING_INDEX_CDF_VAR_NAME, nominal_values(self.cooling_index)),
-            DataProductVariable(PUI_COOLING_INDEX_UNCERTAINTY_CDF_VAR_NAME, std_devs(self.cooling_index)),
-            DataProductVariable(PUI_IONIZATION_RATE_CDF_VAR_NAME, nominal_values(self.ionization_rate)),
-            DataProductVariable(PUI_IONIZATION_RATE_UNCERTAINTY_CDF_VAR_NAME, std_devs(self.ionization_rate)),
-            DataProductVariable(PUI_CUTOFF_SPEED_CDF_VAR_NAME, nominal_values(self.cutoff_speed)),
-            DataProductVariable(PUI_CUTOFF_SPEED_UNCERTAINTY_CDF_VAR_NAME, std_devs(self.cutoff_speed)),
-            DataProductVariable(PUI_BACKGROUND_COUNT_RATE_CDF_VAR_NAME, nominal_values(self.background_rate)),
-            DataProductVariable(PUI_BACKGROUND_COUNT_RATE_UNCERTAINTY_CDF_VAR_NAME, std_devs(self.background_rate)),
+            DataProductVariable(
+                EPOCH_DELTA_CDF_VAR_NAME,
+                np.full_like(self.epoch, FIVE_MINUTES_IN_NANOSECONDS),
+            ),
+            DataProductVariable(
+                PUI_COOLING_INDEX_CDF_VAR_NAME, nominal_values(self.cooling_index)
+            ),
+            DataProductVariable(
+                PUI_COOLING_INDEX_UNCERTAINTY_CDF_VAR_NAME, std_devs(self.cooling_index)
+            ),
+            DataProductVariable(
+                PUI_IONIZATION_RATE_CDF_VAR_NAME, nominal_values(self.ionization_rate)
+            ),
+            DataProductVariable(
+                PUI_IONIZATION_RATE_UNCERTAINTY_CDF_VAR_NAME,
+                std_devs(self.ionization_rate),
+            ),
+            DataProductVariable(
+                PUI_CUTOFF_SPEED_CDF_VAR_NAME, nominal_values(self.cutoff_speed)
+            ),
+            DataProductVariable(
+                PUI_CUTOFF_SPEED_UNCERTAINTY_CDF_VAR_NAME, std_devs(self.cutoff_speed)
+            ),
+            DataProductVariable(
+                PUI_BACKGROUND_COUNT_RATE_CDF_VAR_NAME,
+                nominal_values(self.background_rate),
+            ),
+            DataProductVariable(
+                PUI_BACKGROUND_COUNT_RATE_UNCERTAINTY_CDF_VAR_NAME,
+                std_devs(self.background_rate),
+            ),
             DataProductVariable(PUI_DENSITY_CDF_VAR_NAME, nominal_values(self.density)),
-            DataProductVariable(PUI_DENSITY_UNCERTAINTY_CDF_VAR_NAME, std_devs(self.density)),
-            DataProductVariable(PUI_TEMPERATURE_CDF_VAR_NAME, nominal_values(self.temperature)),
-            DataProductVariable(PUI_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME, std_devs(self.temperature)),
+            DataProductVariable(
+                PUI_DENSITY_UNCERTAINTY_CDF_VAR_NAME, std_devs(self.density)
+            ),
+            DataProductVariable(
+                PUI_TEMPERATURE_CDF_VAR_NAME, nominal_values(self.temperature)
+            ),
+            DataProductVariable(
+                PUI_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME, std_devs(self.temperature)
+            ),
             DataProductVariable(SWAPI_QUALITY_FLAGS_CDF_VAR_NAME, self.quality_flags),
         ]
 
diff --git a/imap_l3_processing/swapi/l3a/science/calculate_alpha_solar_wind_speed.py b/imap_l3_processing/swapi/l3a/science/calculate_alpha_solar_wind_speed.py
deleted file mode 100644
index 426ea3ef2..000000000
--- a/imap_l3_processing/swapi/l3a/science/calculate_alpha_solar_wind_speed.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import numpy as np
-from uncertainties import umath
-
-from imap_l3_processing.constants import ALPHA_PARTICLE_CHARGE_COULOMBS, ALPHA_PARTICLE_MASS_KG, METERS_PER_KILOMETER
-from imap_l3_processing.swapi.l3a.science.speed_calculation import get_peak_indices, interpolate_energy, \
-    find_peak_center_of_mass_index, extract_coarse_sweep
-
-
-def get_alpha_peak_indices(count_rates, energies) -> slice:
-    energies = np.asarray(energies)
-    assert np.all(energies[:-1] >= energies[1:]), "Energies must be decreasing"
-    proton_peak_index = get_peak_indices(count_rates, 0).start
-
-    def find_start_of_alpha_particle_peak():
-        last_count_rate = count_rates[proton_peak_index]
-        for i in reversed(range(proton_peak_index - 1)):
-            if count_rates[i] > last_count_rate:
-                return i
-            last_count_rate = count_rates[i]
-
-    start_of_alpha_peak = find_start_of_alpha_particle_peak()
-    if start_of_alpha_peak is None:
-        raise Exception("Alpha peak not found")
-    indices = np.arange(len(count_rates))
-    after_proton_peak = indices <= start_of_alpha_peak
-    before_4x_proton_energy = energies <= 4 * energies[proton_peak_index]
-    mask = after_proton_peak & before_4x_proton_energy
-    return get_peak_indices(count_rates, 2, mask)
-
-
-def calculate_alpha_center_of_mass(coincidence_count_rates, energies):
-    alpha_particle_peak_slice = get_alpha_peak_indices(coincidence_count_rates, energies)
-    center_of_mass_index = find_peak_center_of_mass_index(alpha_particle_peak_slice, coincidence_count_rates, 0.001, 3)
-    energy_at_center_of_mass = interpolate_energy(center_of_mass_index, energies)
-
-    return energy_at_center_of_mass
-
-
-def calculate_sw_speed_alpha(energy):
-    return umath.sqrt(2 * energy * ALPHA_PARTICLE_CHARGE_COULOMBS / ALPHA_PARTICLE_MASS_KG) / METERS_PER_KILOMETER
-
-
-def calculate_alpha_solar_wind_speed(coincidence_count_rates, energies):
-    average_coin_rates, energies = calculate_combined_sweeps(coincidence_count_rates, energies)
-
-    energy_at_center_of_mass = calculate_alpha_center_of_mass(average_coin_rates, energies)
-    speed = calculate_sw_speed_alpha(energy_at_center_of_mass)
-    return speed
-
-
-def calculate_combined_sweeps(coincidence_count_rates, energies):
-    energies = np.mean(extract_coarse_sweep(energies), axis=0)
-    coincidence_count_rates = extract_coarse_sweep(coincidence_count_rates)
-    average_coin_rates = np.sum(coincidence_count_rates, axis=0) / len(coincidence_count_rates)
-    return average_coin_rates, energies
diff --git a/imap_l3_processing/swapi/l3a/science/calculate_alpha_solar_wind_temperature_and_density.py b/imap_l3_processing/swapi/l3a/science/calculate_alpha_solar_wind_temperature_and_density.py
deleted file mode 100644
index c0e6476d9..000000000
--- a/imap_l3_processing/swapi/l3a/science/calculate_alpha_solar_wind_temperature_and_density.py
+++ /dev/null
@@ -1,114 +0,0 @@
-from dataclasses import dataclass
-from pathlib import Path
-
-import numpy as np
-import scipy
-import uncertainties
-from numpy import ndarray
-from scipy.interpolate import LinearNDInterpolator
-from scipy.special import erf
-from uncertainties import correlated_values, ufloat
-from uncertainties.unumpy import uarray, nominal_values, std_devs
-
-from imap_l3_processing.constants import BOLTZMANN_CONSTANT_JOULES_PER_KELVIN, METERS_PER_KILOMETER, \
-    CENTIMETERS_PER_METER, ALPHA_PARTICLE_CHARGE_COULOMBS, ALPHA_PARTICLE_MASS_KG, SWAPI_EFFECTIVE_AREA_CM2
-from imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_speed import calculate_combined_sweeps, \
-    get_alpha_peak_indices
-from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
-
-
-class AlphaTemperatureDensityCalibrationTable:
-    def __init__(self, lookup_table_array: np.ndarray):
-        coords = lookup_table_array[:, [0, 1, 3]]
-        values = lookup_table_array[:, [2, 4]]
-
-        self.interp = LinearNDInterpolator(coords, values)
-
-    @classmethod
-    def from_file(cls, file_path: Path):
-        lookup_table = np.loadtxt(file_path)
-        return cls(lookup_table)
-
-    @uncertainties.wrap
-    def lookup_temperature(self, sw_speed, fit_density, fit_temperature):
-        return self.interp(sw_speed, fit_density, fit_temperature)[1]
-
-    @uncertainties.wrap
-    def lookup_density(self, sw_speed, fit_density, fit_temperature):
-        return self.interp(sw_speed, fit_density, fit_temperature)[0]
-
-
-def alpha_count_rate_model(efficiency, ev_per_q, density_per_cm3, temperature, bulk_flow_speed_km_per_s):
-    density_per_m3 = density_per_cm3 * CENTIMETERS_PER_METER ** 3
-    bulk_flow_speed_meters_per_s = bulk_flow_speed_km_per_s * METERS_PER_KILOMETER
-    energy = ev_per_q * ALPHA_PARTICLE_CHARGE_COULOMBS
-    k = BOLTZMANN_CONSTANT_JOULES_PER_KELVIN
-    a_eff_cm2 = efficiency * SWAPI_EFFECTIVE_AREA_CM2
-    a_eff_m2 = a_eff_cm2 / CENTIMETERS_PER_METER ** 2
-
-    delta_e_over_e = 0.085
-    delta_v_over_v = 1 / 2 * delta_e_over_e
-    delta_phi_degrees = 30
-
-    m = ALPHA_PARTICLE_MASS_KG
-    v_th = np.sqrt(2 * k * temperature / m)
-    beta = 1 / v_th ** 2
-    v_e = np.sqrt(2 * energy / m)
-    result = (density_per_m3 * a_eff_m2 * (beta / np.pi) ** (3 / 2) * np.exp(
-        -beta * (v_e ** 2 + bulk_flow_speed_meters_per_s ** 2 - 2 * v_e * bulk_flow_speed_meters_per_s))
-              * np.sqrt(np.pi / (beta * bulk_flow_speed_meters_per_s * v_e))
-              * erf(np.sqrt(beta * bulk_flow_speed_meters_per_s * v_e) * np.radians(delta_phi_degrees / 2))
-              * v_e ** 4 * delta_v_over_v * np.arcsin(v_th / v_e))
-
-    return result
-
-
-def calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps(
-        table: AlphaTemperatureDensityCalibrationTable,
-        alpha_sw_speed: ufloat,
-        count_rates: uarray,
-        energies: ndarray,
-        efficiency: float,
-):
-    average_count_rates, energies = calculate_combined_sweeps(count_rates, energies)
-
-    alpha_particle_peak_slice = get_alpha_peak_indices(average_count_rates, energies)
-
-    peak_energies = energies[alpha_particle_peak_slice]
-    peak_average_alpha_count_rates = average_count_rates[alpha_particle_peak_slice]
-    at_least_minimum = nominal_values(peak_average_alpha_count_rates) > 0
-    filtered_peak_count_rates = peak_average_alpha_count_rates[at_least_minimum]
-    filtered_peak_energies = peak_energies[at_least_minimum]
-
-    initial_parameter_guess = [0.15, 3.6e5]
-
-    def model(ev_per_q, density, temperature):
-        return alpha_count_rate_model(efficiency, ev_per_q, density, temperature, nominal_values(alpha_sw_speed))
-
-    values, covariance = scipy.optimize.curve_fit(model,
-                                                  filtered_peak_energies,
-                                                  nominal_values(filtered_peak_count_rates),
-                                                  sigma=std_devs(filtered_peak_count_rates),
-                                                  absolute_sigma=True,
-                                                  bounds=[[0, 0], [np.inf, np.inf]],
-                                                  p0=initial_parameter_guess)
-    residual = abs(model(filtered_peak_energies, *values) - nominal_values(filtered_peak_count_rates))
-    reduced_chisq = np.sum(np.square(residual / std_devs(filtered_peak_count_rates))) / (
-            len(filtered_peak_energies) - 2)
-    bad_fit_flag = SwapiL3Flags.NONE
-    if reduced_chisq > 10:
-        bad_fit_flag = SwapiL3Flags.HI_CHI_SQ
-    pre_lut_density, pre_lut_temperature = correlated_values(values, covariance)
-    density = table.lookup_density(alpha_sw_speed, pre_lut_density, pre_lut_temperature)
-    temperature = table.lookup_temperature(alpha_sw_speed, pre_lut_density, pre_lut_temperature)
-
-    return AlphaSolarWindTemperatureAndDensity(temperature, density, pre_lut_temperature, pre_lut_density, bad_fit_flag)
-
-
-@dataclass
-class AlphaSolarWindTemperatureAndDensity:
-    temperature: ufloat
-    density: ufloat
-    pre_lut_temperature: ufloat
-    pre_lut_density: ufloat
-    bad_fit_flag: int = SwapiL3Flags.NONE
diff --git a/imap_l3_processing/swapi/l3a/science/calculate_pickup_ion.py b/imap_l3_processing/swapi/l3a/science/calculate_pickup_ion.py
index 69630b720..f20ef224e 100644
--- a/imap_l3_processing/swapi/l3a/science/calculate_pickup_ion.py
+++ b/imap_l3_processing/swapi/l3a/science/calculate_pickup_ion.py
@@ -14,56 +14,100 @@
 from uncertainties import ufloat
 from uncertainties.unumpy import uarray
 
-from imap_l3_processing.constants import PROTON_MASS_KG, PROTON_CHARGE_COULOMBS, \
-    HE_PUI_PARTICLE_MASS_KG, PUI_PARTICLE_CHARGE_COULOMBS, ONE_AU_IN_KM, \
-    METERS_PER_KILOMETER, CENTIMETERS_PER_METER, ONE_SECOND_IN_NANOSECONDS, BOLTZMANN_CONSTANT_JOULES_PER_KELVIN
-from imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_speed import calculate_combined_sweeps
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_speed import calculate_sw_speed
-from imap_l3_processing.swapi.l3a.science.density_of_neutral_helium_lookup_table import \
-    DensityOfNeutralHeliumLookupTable
+from imap_l3_processing.constants import (
+    PROTON_MASS_KG,
+    PROTON_CHARGE_COULOMBS,
+    HE_PUI_PARTICLE_MASS_KG,
+    PUI_PARTICLE_CHARGE_COULOMBS,
+    ONE_AU_IN_KM,
+    METERS_PER_KILOMETER,
+    CENTIMETERS_PER_METER,
+    ONE_SECOND_IN_NANOSECONDS,
+    BOLTZMANN_CONSTANT_JOULES_PER_KELVIN,
+)
+from imap_l3_processing.swapi.constants import SWAPI_COARSE_SWEEP_BINS
+from imap_l3_processing.swapi.l3a.utils import calculate_sw_speed
+from imap_l3_processing.swapi.l3a.science.density_of_neutral_helium_lookup_table import (
+    DensityOfNeutralHeliumLookupTable,
+)
 from imap_l3_processing.swapi.l3a.science.inflow_vector import InflowVector
-from imap_l3_processing.swapi.l3b.science.efficiency_calibration_table import EfficiencyCalibrationTable
-from imap_l3_processing.swapi.l3b.science.geometric_factor_calibration_table import GeometricFactorCalibrationTable
-from imap_l3_processing.swapi.l3b.science.instrument_response_lookup_table import InstrumentResponseLookupTable, \
-    InstrumentResponseLookupTableCollection
+from imap_l3_processing.swapi.l3b.science.efficiency_calibration_table import (
+    EfficiencyCalibrationTable,
+)
+from imap_l3_processing.swapi.l3b.science.geometric_factor_calibration_table import (
+    GeometricFactorCalibrationTable,
+)
+from imap_l3_processing.swapi.l3b.science.instrument_response_lookup_table import (
+    InstrumentResponseLookupTable,
+    InstrumentResponseLookupTableCollection,
+)
 from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
 import numdifftools as ndt
 
 
-def calculate_pickup_ion_values(instrument_response_lookup_table, geometric_factor_calibration_table,
-                                energy: np.ndarray[float], count_rates: uarray, center_of_epoch: int,
-                                sw_velocity_vector: ndarray,
-                                density_of_neutral_helium_lookup_table: DensityOfNeutralHeliumLookupTable,
-                                efficiency_table: EfficiencyCalibrationTable, hydrogen_inflow_vector: InflowVector,
-                                helium_inflow_vector: InflowVector) -> FittingParameters:
-    ephemeris_time = spiceypy.unitim(center_of_epoch / ONE_SECOND_IN_NANOSECONDS, "TT", "ET")
+def calculate_pickup_ion_values(
+    instrument_response_lookup_table,
+    geometric_factor_calibration_table,
+    energy: np.ndarray[float],
+    count_rates: uarray,
+    center_of_epoch: int,
+    sw_velocity_vector: ndarray,
+    density_of_neutral_helium_lookup_table: DensityOfNeutralHeliumLookupTable,
+    efficiency_table: EfficiencyCalibrationTable,
+    hydrogen_inflow_vector: InflowVector,
+    helium_inflow_vector: InflowVector,
+) -> FittingParameters:
+    ephemeris_time = spiceypy.unitim(
+        center_of_epoch / ONE_SECOND_IN_NANOSECONDS, "TT", "ET"
+    )
     sw_velocity = np.linalg.norm(sw_velocity_vector)
 
     energy_labels = range(62, 0, -1)
-    lower_energy_cutoff = 1.25 * calculate_pui_energy_cutoff(PROTON_MASS_KG, ephemeris_time, sw_velocity_vector,
-                                                             hydrogen_inflow_vector)
-    upper_energy_cutoff = 1.2 * calculate_pui_energy_cutoff(HE_PUI_PARTICLE_MASS_KG, ephemeris_time, sw_velocity_vector,
-                                                            helium_inflow_vector)
+    lower_energy_cutoff = 1.25 * calculate_pui_energy_cutoff(
+        PROTON_MASS_KG, ephemeris_time, sw_velocity_vector, hydrogen_inflow_vector
+    )
+    upper_energy_cutoff = 1.2 * calculate_pui_energy_cutoff(
+        HE_PUI_PARTICLE_MASS_KG,
+        ephemeris_time,
+        sw_velocity_vector,
+        helium_inflow_vector,
+    )
     sweep_count = len(count_rates)
-    average_count_rates, energies = calculate_combined_sweeps(count_rates, energy)
-
-    extracted_energy_labels, extracted_energies, extracted_count_rates = extract_pui_energy_bins(energy_labels,
-                                                                                                 energies,
-                                                                                                 average_count_rates,
-                                                                                                 lower_energy_cutoff,
-                                                                                                 upper_energy_cutoff)
-    model_count_rate_calculator = ModelCountRateCalculator(instrument_response_lookup_table,
-                                                           geometric_factor_calibration_table, sw_velocity_vector,
-                                                           density_of_neutral_helium_lookup_table, efficiency_table,
-                                                           helium_inflow_vector)
+    average_count_rates = np.mean(count_rates[:, SWAPI_COARSE_SWEEP_BINS], axis=0)
+    energies = np.mean(energy[:, SWAPI_COARSE_SWEEP_BINS], axis=0)
+
+    extracted_energy_labels, extracted_energies, extracted_count_rates = (
+        extract_pui_energy_bins(
+            energy_labels,
+            energies,
+            average_count_rates,
+            lower_energy_cutoff,
+            upper_energy_cutoff,
+        )
+    )
+    model_count_rate_calculator = ModelCountRateCalculator(
+        instrument_response_lookup_table,
+        geometric_factor_calibration_table,
+        sw_velocity_vector,
+        density_of_neutral_helium_lookup_table,
+        efficiency_table,
+        helium_inflow_vector,
+    )
     indices = list(zip(extracted_energy_labels, extracted_energies))
 
-    def make_parameters(cooling_index, ionization_rate, cutoff_speed, background_count_rate) -> Parameters:
+    def make_parameters(
+        cooling_index, ionization_rate, cutoff_speed, background_count_rate
+    ) -> Parameters:
         params = Parameters()
-        params.add('cooling_index', value=cooling_index, min=1.0, max=5.0)
-        params.add('ionization_rate', value=ionization_rate, min=0.6e-9, max=8.0e-7)
-        params.add('cutoff_speed', value=cutoff_speed, min=sw_velocity * .8, max=sw_velocity * 1.2)
-        params.add('background_count_rate', value=background_count_rate, min=0, max=0.2)
+        params.add("cooling_index", value=cooling_index, min=1.0, max=5.0)
+        params.add("ionization_rate", value=ionization_rate, min=0.6e-9, max=8.0e-7)
+        params.add(
+            "cutoff_speed",
+            value=cutoff_speed,
+            min=sw_velocity * 0.8,
+            max=sw_velocity * 1.2,
+        )
+        params.add("background_count_rate", value=background_count_rate, min=0, max=0.2)
         return params
 
     params = make_parameters(1.50, 1e-7, sw_velocity, 0.1)
@@ -73,35 +117,43 @@ def map_to_internal(value, param):
 
     def map_param_values_to_internal_values(ci, ir, cs, bcr):
         return [
-            map_to_internal(ci, params['cooling_index']),
-            map_to_internal(ir, params['ionization_rate']),
-            map_to_internal(cs, params['cutoff_speed']),
-            map_to_internal(bcr, params['background_count_rate']),
+            map_to_internal(ci, params["cooling_index"]),
+            map_to_internal(ir, params["ionization_rate"]),
+            map_to_internal(cs, params["cutoff_speed"]),
+            map_to_internal(bcr, params["background_count_rate"]),
         ]
 
-    initial_simplex=np.array([
-        map_param_values_to_internal_values(1.5, 1e-7, sw_velocity, 0.1),
-        map_param_values_to_internal_values(5.0, 1e-7, sw_velocity, 0.1),
-        map_param_values_to_internal_values(1.5, 2.1e-7, sw_velocity, 0.1),
-        map_param_values_to_internal_values(1.5, 1e-7, sw_velocity * 1.2, 0.1),
-        map_param_values_to_internal_values(1.5, 1e-7, sw_velocity, 0.2),
-    ])
+    initial_simplex = np.array(
+        [
+            map_param_values_to_internal_values(1.5, 1e-7, sw_velocity, 0.1),
+            map_param_values_to_internal_values(5.0, 1e-7, sw_velocity, 0.1),
+            map_param_values_to_internal_values(1.5, 2.1e-7, sw_velocity, 0.1),
+            map_param_values_to_internal_values(1.5, 1e-7, sw_velocity * 1.2, 0.1),
+            map_param_values_to_internal_values(1.5, 1e-7, sw_velocity, 0.2),
+        ]
+    )
     minimizer = lmfit.Minimizer(
-        calc_chi_squared_lm_fit, params,
-                                fcn_args=(extracted_count_rates, indices, model_count_rate_calculator, ephemeris_time,
-                                sweep_count),
-                                scale_covar=False,
-                                options=dict(initial_simplex=initial_simplex),
-                                )
+        calc_chi_squared_lm_fit,
+        params,
+        fcn_args=(
+            extracted_count_rates,
+            indices,
+            model_count_rate_calculator,
+            ephemeris_time,
+            sweep_count,
+        ),
+        scale_covar=False,
+        options=dict(initial_simplex=initial_simplex),
+    )
     result = minimizer.minimize(method="nelder")
     flags = SwapiL3Flags.NONE
     if result.redchi > 10:
-        flags |= SwapiL3Flags.HI_CHI_SQ
+        flags |= SwapiL3Flags.BAD_FIT
 
     param_vals = result.uvars
     if result.uvars is None:
         flags |= SwapiL3Flags.PUI_FIT_MISSING_UNCERTAINTY
-        Hfun = ndt.Hessian(minimizer.penalty, step=1.e-4)
+        Hfun = ndt.Hessian(minimizer.penalty, step=1.0e-4)
         try:
             hessian_ndt = Hfun(result.x)
             cov_x = inv(hessian_ndt) * 2.0
@@ -111,77 +163,132 @@ def map_param_values_to_internal_values(ci, ir, cs, bcr):
 
             nominal_value_by_param_name = result.params.valuesdict()
             for var_name, uncertainty in zip(result.var_names, uncertainties):
-                param_vals[var_name] = ufloat(nominal_value_by_param_name[var_name], uncertainty)
+                param_vals[var_name] = ufloat(
+                    nominal_value_by_param_name[var_name], uncertainty
+                )
         except:
-            param_vals = {k: ufloat(v, np.nan) for k, v in result.params.valuesdict().items()}
-
-
-    return FittingParameters(param_vals["cooling_index"], param_vals["ionization_rate"], param_vals["cutoff_speed"],
-                             param_vals["background_count_rate"], flags)
-
-
-def calculate_helium_pui_density(epoch: int,
-                                 sw_velocity_vector: ndarray,
-                                 density_of_neutral_helium_lookup_table: DensityOfNeutralHeliumLookupTable,
-                                 fitting_params: FittingParameters, helium_inflow_vector: InflowVector) -> float:
+            param_vals = {
+                k: ufloat(v, np.nan) for k, v in result.params.valuesdict().items()
+            }
+
+    return FittingParameters(
+        param_vals["cooling_index"],
+        param_vals["ionization_rate"],
+        param_vals["cutoff_speed"],
+        param_vals["background_count_rate"],
+        flags,
+    )
+
+
+def calculate_helium_pui_density(
+    epoch: int,
+    sw_velocity_vector: ndarray,
+    density_of_neutral_helium_lookup_table: DensityOfNeutralHeliumLookupTable,
+    fitting_params: FittingParameters,
+    helium_inflow_vector: InflowVector,
+) -> float:
     @uncertainties.wrap
-    def calculate(cooling_index: float,
-                  ionization_rate: float,
-                  cutoff_speed: float,
-                  background_count_rate: float):
+    def calculate(
+        cooling_index: float,
+        ionization_rate: float,
+        cutoff_speed: float,
+        background_count_rate: float,
+    ):
         fitting_params = FittingParameters(
             cooling_index, ionization_rate, cutoff_speed, background_count_rate
         )
         ephemeris_time = spiceypy.unitim(epoch / ONE_SECOND_IN_NANOSECONDS, "TT", "ET")
-        model = build_forward_model(fitting_params, ephemeris_time, sw_velocity_vector,
-                                    density_of_neutral_helium_lookup_table, helium_inflow_vector)
-        lower_discontinuity = (density_of_neutral_helium_lookup_table.get_minimum_distance() / (
-                model.distance_km / ONE_AU_IN_KM)) ** (
-                                      1 / fitting_params.cooling_index) * fitting_params.cutoff_speed
+        model = build_forward_model(
+            fitting_params,
+            ephemeris_time,
+            sw_velocity_vector,
+            density_of_neutral_helium_lookup_table,
+            helium_inflow_vector,
+        )
+        lower_discontinuity = (
+            density_of_neutral_helium_lookup_table.get_minimum_distance()
+            / (model.distance_km / ONE_AU_IN_KM)
+        ) ** (1 / fitting_params.cooling_index) * fitting_params.cutoff_speed
         points = (0, lower_discontinuity, fitting_params.cutoff_speed)
 
-        results = scipy.integrate.quad(lambda v: model.f(v) * v * v, 0, fitting_params.cutoff_speed, limit=100,
-                                       points=points)
-        return 4 * np.pi * results[0] / (CENTIMETERS_PER_METER * METERS_PER_KILOMETER) ** 3
-
-    return calculate(fitting_params.cooling_index,
-                     fitting_params.ionization_rate,
-                     fitting_params.cutoff_speed,
-                     fitting_params.background_count_rate)
-
+        results = scipy.integrate.quad(
+            lambda v: model.f(v) * v * v,
+            0,
+            fitting_params.cutoff_speed,
+            limit=100,
+            points=points,
+        )
+        return (
+            4 * np.pi * results[0] / (CENTIMETERS_PER_METER * METERS_PER_KILOMETER) ** 3
+        )
 
-def calculate_helium_pui_temperature(epoch: int,
-                                     sw_velocity_vector: ndarray,
-                                     density_of_neutral_helium_lookup_table: DensityOfNeutralHeliumLookupTable,
-                                     fitting_params: FittingParameters, helium_inflow_vector: InflowVector) -> float:
+    return calculate(
+        fitting_params.cooling_index,
+        fitting_params.ionization_rate,
+        fitting_params.cutoff_speed,
+        fitting_params.background_count_rate,
+    )
+
+
+def calculate_helium_pui_temperature(
+    epoch: int,
+    sw_velocity_vector: ndarray,
+    density_of_neutral_helium_lookup_table: DensityOfNeutralHeliumLookupTable,
+    fitting_params: FittingParameters,
+    helium_inflow_vector: InflowVector,
+) -> float:
     @uncertainties.wrap
-    def calculate(cooling_index: float,
-                  ionization_rate: float,
-                  cutoff_speed: float,
-                  background_count_rate: float):
+    def calculate(
+        cooling_index: float,
+        ionization_rate: float,
+        cutoff_speed: float,
+        background_count_rate: float,
+    ):
         fitting_params = FittingParameters(
             cooling_index, ionization_rate, cutoff_speed, background_count_rate
         )
         ephemeris_time = spiceypy.unitim(epoch / ONE_SECOND_IN_NANOSECONDS, "TT", "ET")
-        model = build_forward_model(fitting_params, ephemeris_time, sw_velocity_vector,
-                                    density_of_neutral_helium_lookup_table, helium_inflow_vector)
-        lower_discontinuity = (density_of_neutral_helium_lookup_table.get_minimum_distance() / (
-                model.distance_km / ONE_AU_IN_KM)) ** (
-                                      1 / fitting_params.cooling_index) * fitting_params.cutoff_speed
+        model = build_forward_model(
+            fitting_params,
+            ephemeris_time,
+            sw_velocity_vector,
+            density_of_neutral_helium_lookup_table,
+            helium_inflow_vector,
+        )
+        lower_discontinuity = (
+            density_of_neutral_helium_lookup_table.get_minimum_distance()
+            / (model.distance_km / ONE_AU_IN_KM)
+        ) ** (1 / fitting_params.cooling_index) * fitting_params.cutoff_speed
         points = (0, lower_discontinuity, fitting_params.cutoff_speed)
 
-        numerator = scipy.integrate.quad(lambda v: model.f(v) * v ** 4, 0, fitting_params.cutoff_speed, points=points,
-                                         limit=100)
-        denominator = scipy.integrate.quad(lambda v: model.f(v) * v ** 2, 0, fitting_params.cutoff_speed, points=points,
-                                           limit=100)
-        return HE_PUI_PARTICLE_MASS_KG / (3 * BOLTZMANN_CONSTANT_JOULES_PER_KELVIN) * \
-            numerator[0] / denominator[0] * \
-            METERS_PER_KILOMETER ** 2
+        numerator = scipy.integrate.quad(
+            lambda v: model.f(v) * v**4,
+            0,
+            fitting_params.cutoff_speed,
+            points=points,
+            limit=100,
+        )
+        denominator = scipy.integrate.quad(
+            lambda v: model.f(v) * v**2,
+            0,
+            fitting_params.cutoff_speed,
+            points=points,
+            limit=100,
+        )
+        return (
+            HE_PUI_PARTICLE_MASS_KG
+            / (3 * BOLTZMANN_CONSTANT_JOULES_PER_KELVIN)
+            * numerator[0]
+            / denominator[0]
+            * METERS_PER_KILOMETER**2
+        )
 
-    return calculate(fitting_params.cooling_index,
-                     fitting_params.ionization_rate,
-                     fitting_params.cutoff_speed,
-                     fitting_params.background_count_rate)
+    return calculate(
+        fitting_params.cooling_index,
+        fitting_params.ionization_rate,
+        fitting_params.cutoff_speed,
+        fitting_params.background_count_rate,
+    )
 
 
 @dataclass
@@ -205,34 +312,53 @@ class ForwardModel:
     def f(self, pickup_ion_speed):
         w = pickup_ion_speed / self.fitting_params.cutoff_speed
         radius_in_au = self.distance_km / ONE_AU_IN_KM
-        neutral_helium_density_per_cm3 = self.density_of_neutral_helium_lookup_table.density(
-            self.psi, radius_in_au * w ** self.fitting_params.cooling_index)
-        neutral_helium_density_per_km3 = neutral_helium_density_per_cm3 * (
-                CENTIMETERS_PER_METER * METERS_PER_KILOMETER) ** 3
+        neutral_helium_density_per_cm3 = (
+            self.density_of_neutral_helium_lookup_table.density(
+                self.psi, radius_in_au * w**self.fitting_params.cooling_index
+            )
+        )
+        neutral_helium_density_per_km3 = (
+            neutral_helium_density_per_cm3
+            * (CENTIMETERS_PER_METER * METERS_PER_KILOMETER) ** 3
+        )
         term1 = self.fitting_params.cooling_index / (4 * np.pi)
-        term2 = (self.fitting_params.ionization_rate * ONE_AU_IN_KM ** 2) / (
-                self.distance_km * self.solar_wind_speed_inertial_frame * self.fitting_params.cutoff_speed ** 3)
+        term2 = (self.fitting_params.ionization_rate * ONE_AU_IN_KM**2) / (
+            self.distance_km
+            * self.solar_wind_speed_inertial_frame
+            * self.fitting_params.cutoff_speed**3
+        )
         term3 = w ** (self.fitting_params.cooling_index - 3)
         term4 = neutral_helium_density_per_km3
         term5 = np.heaviside(1 - w, 0.5)
         return term1 * term2 * term3 * term4 * term5
 
 
-def build_forward_model(fitting_params: FittingParameters, ephemeris_time: float, solar_wind_vector: ndarray,
-                        density_of_neutral_helium_lookup_table: DensityOfNeutralHeliumLookupTable,
-                        helium_inflow_vector: InflowVector) -> ForwardModel:
-    solar_wind_vector_eclipj2000_frame = convert_velocity_relative_to_imap(solar_wind_vector,
-                                                                           ephemeris_time,
-                                                                           "IMAP_DPS",
-                                                                           "ECLIPJ2000")
+def build_forward_model(
+    fitting_params: FittingParameters,
+    ephemeris_time: float,
+    solar_wind_vector: ndarray,
+    density_of_neutral_helium_lookup_table: DensityOfNeutralHeliumLookupTable,
+    helium_inflow_vector: InflowVector,
+) -> ForwardModel:
+    solar_wind_vector_eclipj2000_frame = convert_velocity_relative_to_imap(
+        solar_wind_vector, ephemeris_time, "IMAP_DPS", "ECLIPJ2000"
+    )
     imap_position_eclip2000_frame_state = spiceypy.spkezr(
-        "IMAP", ephemeris_time, "ECLIPJ2000", "NONE", "SUN")[0][0:3]
-    distance_km, longitude, latitude = spiceypy.reclat(imap_position_eclip2000_frame_state)
+        "IMAP", ephemeris_time, "ECLIPJ2000", "NONE", "SUN"
+    )[0][0:3]
+    distance_km, longitude, latitude = spiceypy.reclat(
+        imap_position_eclip2000_frame_state
+    )
     psi = np.rad2deg(longitude) - helium_inflow_vector.longitude_deg_eclipj2000
 
-    return ForwardModel(fitting_params, ephemeris_time,
-                        np.linalg.norm(solar_wind_vector_eclipj2000_frame),
-                        density_of_neutral_helium_lookup_table, distance_km, psi)
+    return ForwardModel(
+        fitting_params,
+        ephemeris_time,
+        np.linalg.norm(solar_wind_vector_eclipj2000_frame),
+        density_of_neutral_helium_lookup_table,
+        distance_km,
+        psi,
+    )
 
 
 @dataclass
@@ -245,56 +371,97 @@ class ModelCountRateCalculator:
     helium_inflow_vector: InflowVector
     _speed_grid_cache: dict = field(default_factory=dict)
 
-    def get_speed_grid(self, response_lookup_table: InstrumentResponseLookupTable, ephemeris_time: float):
+    def get_speed_grid(
+        self,
+        response_lookup_table: InstrumentResponseLookupTable,
+        ephemeris_time: float,
+    ):
 
         key = (id(response_lookup_table), ephemeris_time)
         cached = self._speed_grid_cache.get(key)
         if cached is not None:
             return cached
 
-        speed_inst = calculate_sw_speed(HE_PUI_PARTICLE_MASS_KG, PUI_PARTICLE_CHARGE_COULOMBS,
-                                        response_lookup_table.energy)
+        speed_inst = calculate_sw_speed(
+            HE_PUI_PARTICLE_MASS_KG,
+            PUI_PARTICLE_CHARGE_COULOMBS,
+            response_lookup_table.energy,
+        )
 
-        pui_vector_instrument_frame = calculate_pui_velocity_vector(speed_inst, response_lookup_table.elevation,
-                                                                    response_lookup_table.azimuth)
-        pui_vector_eclipj2000_frame = convert_velocity_relative_to_imap(pui_vector_instrument_frame,
-                                                                        ephemeris_time,
-                                                                        "IMAP_SWAPI", "ECLIPJ2000")
+        pui_vector_instrument_frame = calculate_pui_velocity_vector(
+            speed_inst, response_lookup_table.elevation, response_lookup_table.azimuth
+        )
+        pui_vector_eclipj2000_frame = convert_velocity_relative_to_imap(
+            pui_vector_instrument_frame, ephemeris_time, "IMAP_SWAPI", "ECLIPJ2000"
+        )
 
-        solar_wind_vector_eclipj2000_frame = convert_velocity_relative_to_imap(self.solar_wind_vector,
-                                                                               ephemeris_time,
-                                                                               "IMAP_DPS",
-                                                                               "ECLIPJ2000")
+        solar_wind_vector_eclipj2000_frame = convert_velocity_relative_to_imap(
+            self.solar_wind_vector, ephemeris_time, "IMAP_DPS", "ECLIPJ2000"
+        )
 
-        pui_vector_solar_wind_frame = pui_vector_eclipj2000_frame - solar_wind_vector_eclipj2000_frame
+        pui_vector_solar_wind_frame = (
+            pui_vector_eclipj2000_frame - solar_wind_vector_eclipj2000_frame
+        )
         speed = np.linalg.norm(pui_vector_solar_wind_frame, axis=-1)
         self._speed_grid_cache[key] = speed
         return speed
 
-    def model_count_rate(self, indices_and_energy_centers: list[tuple[int, float]],
-                         fitting_params: FittingParameters, ephemeris_time: float) -> np.ndarray:
-        forward_model = build_forward_model(fitting_params, ephemeris_time, self.solar_wind_vector,
-                                            self.density_of_neutral_helium_lookup_table,
-                                            self.helium_inflow_vector)
+    def model_count_rate(
+        self,
+        indices_and_energy_centers: list[tuple[int, float]],
+        fitting_params: FittingParameters,
+        ephemeris_time: float,
+    ) -> np.ndarray:
+        forward_model = build_forward_model(
+            fitting_params,
+            ephemeris_time,
+            self.solar_wind_vector,
+            self.density_of_neutral_helium_lookup_table,
+            self.helium_inflow_vector,
+        )
         model_count_rates = []
         for energy_bin_index, energy_bin_center in indices_and_energy_centers:
             model_count_rates.append(
-                self.model_one_count_rate(energy_bin_index, energy_bin_center, forward_model)
+                self.model_one_count_rate(
+                    energy_bin_index, energy_bin_center, forward_model
+                )
             )
         return np.array(model_count_rates)
 
-    def model_one_count_rate(self, energy_bin_index, energy_bin_center, forward_model) -> float:
-        response_lookup_table = self.response_lookup_table_collection.get_table_for_energy_bin(energy_bin_index)
-        speed_grid = self.get_speed_grid(response_lookup_table, forward_model.ephemeris_time)
-        integral = model_count_rate_integral(response_lookup_table, forward_model, speed_grid)
-        efficiency = self.efficiency_table.get_alpha_efficiency_for(forward_model.ephemeris_time)
-        geometric_factor = self.geometric_table.lookup_geometric_factor(energy_bin_center)
-        return efficiency * (geometric_factor / 2) * integral + forward_model.fitting_params.background_count_rate
+    def model_one_count_rate(
+        self, energy_bin_index, energy_bin_center, forward_model
+    ) -> float:
+        response_lookup_table = (
+            self.response_lookup_table_collection.get_table_for_energy_bin(
+                energy_bin_index
+            )
+        )
+        speed_grid = self.get_speed_grid(
+            response_lookup_table, forward_model.ephemeris_time
+        )
+        integral = model_count_rate_integral(
+            response_lookup_table, forward_model, speed_grid
+        )
+        efficiency = self.efficiency_table.get_alpha_efficiency_for(
+            forward_model.ephemeris_time
+        )
+        geometric_factor = self.geometric_table.lookup_geometric_factor(
+            energy_bin_center
+        )
+        return (
+            efficiency * (geometric_factor / 2) * integral
+            + forward_model.fitting_params.background_count_rate
+        )
 
 
-def calc_chi_squared_lm_fit(params: Parameters, observed_count_rates: np.ndarray,
-                            indices_and_energy_centers: list[tuple[int, float]], calculator: ModelCountRateCalculator,
-                            ephemeris_time: float, sweep_count: int):
+def calc_chi_squared_lm_fit(
+    params: Parameters,
+    observed_count_rates: np.ndarray,
+    indices_and_energy_centers: list[tuple[int, float]],
+    calculator: ModelCountRateCalculator,
+    ephemeris_time: float,
+    sweep_count: int,
+):
     parvals = params.valuesdict()
 
     cooling_index = parvals["cooling_index"]
@@ -302,18 +469,31 @@ def calc_chi_squared_lm_fit(params: Parameters, observed_count_rates: np.ndarray
     cutoff_speed = parvals["cutoff_speed"]
     background_count_rate = parvals["background_count_rate"]
 
-    fit_params = FittingParameters(cooling_index, ionization_rate, cutoff_speed, background_count_rate)
-    modeled_rates = calculator.model_count_rate(indices_and_energy_centers, fit_params, ephemeris_time)
+    fit_params = FittingParameters(
+        cooling_index, ionization_rate, cutoff_speed, background_count_rate
+    )
+    modeled_rates = calculator.model_count_rate(
+        indices_and_energy_centers, fit_params, ephemeris_time
+    )
 
     modeled_counts = modeled_rates * sweep_count * swapi_l2.SWAPI_LIVETIME
     observed_counts = observed_count_rates * sweep_count * swapi_l2.SWAPI_LIVETIME
-    result = np.sqrt(2 * (modeled_counts - observed_counts + observed_counts * np.log(
-        observed_counts / modeled_counts)))
+    result = np.sqrt(
+        2
+        * (
+            modeled_counts
+            - observed_counts
+            + observed_counts * np.log(observed_counts / modeled_counts)
+        )
+    )
     return result
 
 
-def model_count_rate_integral(response_lookup_table: InstrumentResponseLookupTable, forward_model: ForwardModel,
-                              speed_grid):
+def model_count_rate_integral(
+    response_lookup_table: InstrumentResponseLookupTable,
+    forward_model: ForwardModel,
+    speed_grid,
+):
     count_rates = forward_model.f(speed_grid)
 
     integrals = response_lookup_table.integral_factor * count_rates
@@ -321,8 +501,9 @@ def model_count_rate_integral(response_lookup_table: InstrumentResponseLookupTab
     return np.sum(integrals)
 
 
-def convert_velocity_to_reference_frame(velocity: ndarray, ephemeris_time: float, from_frame: str,
-                                        to_frame: str) -> ndarray:
+def convert_velocity_to_reference_frame(
+    velocity: ndarray, ephemeris_time: float, from_frame: str, to_frame: str
+) -> ndarray:
     rotation_matrix = spiceypy.sxform(from_frame, to_frame, ephemeris_time)
 
     state = velocity[..., np.newaxis]
@@ -332,14 +513,19 @@ def convert_velocity_to_reference_frame(velocity: ndarray, ephemeris_time: float
 
 
 def convert_velocity_relative_to_imap(velocity, ephemeris_time, from_frame, to_frame):
-    velocity_in_target_frame_relative_to_imap = convert_velocity_to_reference_frame(velocity, ephemeris_time,
-                                                                                    from_frame, to_frame)
-    imap_velocity = spiceypy.spkezr("IMAP", ephemeris_time, to_frame, "NONE", "SUN")[0][3:6]
+    velocity_in_target_frame_relative_to_imap = convert_velocity_to_reference_frame(
+        velocity, ephemeris_time, from_frame, to_frame
+    )
+    imap_velocity = spiceypy.spkezr("IMAP", ephemeris_time, to_frame, "NONE", "SUN")[0][
+        3:6
+    ]
 
     return velocity_in_target_frame_relative_to_imap + imap_velocity
 
 
-def calculate_velocity_vector(sw_speed: ndarray, elevation: ndarray, azimuth: ndarray) -> np.ndarray:
+def calculate_velocity_vector(
+    sw_speed: ndarray, elevation: ndarray, azimuth: ndarray
+) -> np.ndarray:
     elevation_radians = np.deg2rad(elevation)
     azimuth_radians = np.deg2rad(azimuth)
     z = sw_speed * np.sin(elevation_radians)
@@ -349,59 +535,83 @@ def calculate_velocity_vector(sw_speed: ndarray, elevation: ndarray, azimuth: nd
     return np.transpose([x, y, z])
 
 
-def calculate_pui_velocity_vector(speed: ndarray, elevation: ndarray, azimuth: ndarray) -> np.ndarray:
+def calculate_pui_velocity_vector(
+    speed: ndarray, elevation: ndarray, azimuth: ndarray
+) -> np.ndarray:
     y_axis_azimuth = 90
     return calculate_velocity_vector(-speed, elevation, y_axis_azimuth - azimuth)
 
 
-def calculate_pui_energy_cutoff(particle_mass: float, ephemeris_time: float, sw_velocity_in_imap_frame,
-                                particle_inflow_vector: InflowVector):
-    imap_velocity = spiceypy.spkezr("IMAP", ephemeris_time, "ECLIPJ2000", "NONE", "SUN")[0][
-                    3:6]
+def calculate_pui_energy_cutoff(
+    particle_mass: float,
+    ephemeris_time: float,
+    sw_velocity_in_imap_frame,
+    particle_inflow_vector: InflowVector,
+):
+    imap_velocity = spiceypy.spkezr(
+        "IMAP", ephemeris_time, "ECLIPJ2000", "NONE", "SUN"
+    )[0][3:6]
     solar_wind_velocity = convert_velocity_relative_to_imap(
-        sw_velocity_in_imap_frame, ephemeris_time, "IMAP_DPS", "ECLIPJ2000")
-    particle_velocity = spiceypy.latrec(-particle_inflow_vector.speed_km_per_s,
-                                        particle_inflow_vector.longitude_deg_eclipj2000,
-                                        particle_inflow_vector.latitude_deg_eclipj2000)
-
-    particle_velocity_cutoff_vector = solar_wind_velocity - particle_velocity - imap_velocity
+        sw_velocity_in_imap_frame, ephemeris_time, "IMAP_DPS", "ECLIPJ2000"
+    )
+    particle_velocity = spiceypy.latrec(
+        -particle_inflow_vector.speed_km_per_s,
+        particle_inflow_vector.longitude_deg_eclipj2000,
+        particle_inflow_vector.latitude_deg_eclipj2000,
+    )
+
+    particle_velocity_cutoff_vector = (
+        solar_wind_velocity - particle_velocity - imap_velocity
+    )
     particle_speed_cutoff = np.linalg.norm(particle_velocity_cutoff_vector)
-    return (0.5 * (particle_mass / PROTON_CHARGE_COULOMBS) * (2 * particle_speed_cutoff * METERS_PER_KILOMETER) ** 2)
-
-
-def extract_pui_energy_bins(energy_bin_labels, energies, observed_count_rates, lower_energy_cutoff,
-                            upper_energy_cutoff):
+    return (
+        0.5
+        * (particle_mass / PROTON_CHARGE_COULOMBS)
+        * (2 * particle_speed_cutoff * METERS_PER_KILOMETER) ** 2
+    )
+
+
+def extract_pui_energy_bins(
+    energy_bin_labels,
+    energies,
+    observed_count_rates,
+    lower_energy_cutoff,
+    upper_energy_cutoff,
+):
     extracted_energy_bins = []
     count_rates = []
     extracted_energy_bin_labels = []
 
-    for label, energy, count_rate in zip(energy_bin_labels, energies, observed_count_rates):
-        if energy > lower_energy_cutoff and energy < upper_energy_cutoff and count_rate > 0:
+    for label, energy, count_rate in zip(
+        energy_bin_labels, energies, observed_count_rates
+    ):
+        if (
+            energy > lower_energy_cutoff
+            and energy < upper_energy_cutoff
+            and count_rate > 0
+        ):
             extracted_energy_bins.append(energy)
             count_rates.append(count_rate)
             extracted_energy_bin_labels.append(label)
 
-    return np.array(extracted_energy_bin_labels), np.array(extracted_energy_bins), np.array(count_rates)
+    return (
+        np.array(extracted_energy_bin_labels),
+        np.array(extracted_energy_bins),
+        np.array(count_rates),
+    )
 
 
-def calculate_solar_wind_velocity_vector(speeds: ndarray, deflection_angle: ndarray, clock_angle: ndarray) -> ndarray:
-    elevation_angle = 90 - deflection_angle
-    return calculate_velocity_vector(-speeds, elevation_angle, clock_angle)
-
-
-def calculate_ten_minute_velocities(speeds: ndarray, deflection_angle: ndarray, clock_angle: ndarray,
-                                    quality_flags: list[SwapiL3Flags]) -> (ndarray, ndarray):
-    velocity_vector = calculate_solar_wind_velocity_vector(speeds, deflection_angle, clock_angle)
-    left_slice = 0
+def calculate_ten_minute_velocities(
+    velocity_vectors_dps: ndarray,
+    quality_flags: list[SwapiL3Flags],
+) -> (ndarray, ndarray):
     chunked_velocities = []
     chunked_quality_flags = []
-    while left_slice < len(velocity_vector):
+    for left_slice in range(0, len(velocity_vectors_dps), 10):
         ten_min_slice = slice(left_slice, left_slice + 10)
-        ten_min_quality_flag = np.bitwise_or.reduce(quality_flags[ten_min_slice])
-
-        chunked_velocities.append(np.mean(velocity_vector[ten_min_slice], axis=0))
-        chunked_quality_flags.append(ten_min_quality_flag)
-
-        left_slice += 10
+        chunked_velocities.append(
+            np.mean(velocity_vectors_dps[ten_min_slice], axis=0)
+        )
+        chunked_quality_flags.append(np.bitwise_or.reduce(quality_flags[ten_min_slice]))
 
     return np.array(chunked_velocities), np.array(chunked_quality_flags)
diff --git a/imap_l3_processing/swapi/l3a/science/calculate_proton_solar_wind_clock_and_deflection_angles.py b/imap_l3_processing/swapi/l3a/science/calculate_proton_solar_wind_clock_and_deflection_angles.py
deleted file mode 100644
index b515acbd6..000000000
--- a/imap_l3_processing/swapi/l3a/science/calculate_proton_solar_wind_clock_and_deflection_angles.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import numpy as np
-import uncertainties
-from scipy.interpolate import LinearNDInterpolator
-from uncertainties import ufloat
-
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_speed import estimate_deflection_and_clock_angles
-
-
-class ClockAngleCalibrationTable:
-    def __init__(self, calibration_table: np.ndarray):
-        coords = calibration_table[:, 0:2]
-        values = calibration_table[:, 2:4]
-        self.interp = LinearNDInterpolator(coords, values)
-
-    @uncertainties.wrap
-    def lookup_clock_offset(self, sw_speed: float, a_over_b: float):
-        return self.interp(sw_speed, a_over_b)[1]
-
-    @uncertainties.wrap
-    def lookup_flow_deflection(self, sw_speed: float, a_over_b: float):
-        return self.interp(sw_speed, a_over_b)[0]
-
-    @classmethod
-    def from_file(cls, file):
-        data = np.loadtxt(file)
-        return cls(data)
-
-
-def calculate_clock_angle(lookup_table: ClockAngleCalibrationTable, sw_speed, a, phi, b):
-    a_over_b = a / b
-    phi_offset = lookup_table.lookup_clock_offset(sw_speed, a_over_b)
-
-    if np.isnan(phi_offset.nominal_value):
-        _, clock_angle = estimate_deflection_and_clock_angles(sw_speed.nominal_value)
-        return clock_angle
-    else:
-        return (phi - phi_offset) % 360
-
-
-def calculate_deflection_angle(lookup_table, sw_speed, a, phi, b):
-    a_over_b = a / b
-    deflection_angle = lookup_table.lookup_flow_deflection(sw_speed, a_over_b)
-
-    if np.isnan(deflection_angle.nominal_value):
-        return ufloat(5, 45)
-    return deflection_angle
diff --git a/imap_l3_processing/swapi/l3a/science/calculate_proton_solar_wind_speed.py b/imap_l3_processing/swapi/l3a/science/calculate_proton_solar_wind_speed.py
deleted file mode 100644
index 536da6bf1..000000000
--- a/imap_l3_processing/swapi/l3a/science/calculate_proton_solar_wind_speed.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import numpy as np
-import scipy
-import spiceypy
-import uncertainties
-from uncertainties import correlated_values, unumpy, umath, wrap
-from uncertainties.unumpy import uarray, nominal_values, std_devs
-
-from imap_l3_processing.constants import PROTON_CHARGE_COULOMBS, PROTON_MASS_KG, METERS_PER_KILOMETER, \
-    ONE_SECOND_IN_NANOSECONDS
-from imap_l3_processing.swapi.l3a.science.speed_calculation import get_peak_indices, find_peak_center_of_mass_index, \
-    interpolate_energy, extract_coarse_sweep
-
-
-def sine_fit_function(spin_phase_angle, a, phi, b):
-    return a * np.sin(np.deg2rad(phi - spin_phase_angle)) + b
-
-
-def fit_energy_per_charge_peak_variations(centers_of_mass, spin_phase_angles):
-    nominal_centers_of_mass = nominal_values(centers_of_mass)
-    min_mass_energy = np.min(nominal_centers_of_mass)
-    max_mass_energy = np.max(nominal_centers_of_mass)
-    peak_angle = nominal_values(spin_phase_angles[np.argmax(centers_of_mass)])
-
-    initial_parameter_guess = [(max_mass_energy - min_mass_energy) / 2, peak_angle + 90,
-                               np.mean(nominal_centers_of_mass)]
-
-    nominal_spin_phase_angles = nominal_values(spin_phase_angles)
-
-    (a, phi, b), pcov = scipy.optimize.curve_fit(
-        sine_fit_function, nominal_spin_phase_angles, nominal_centers_of_mass,
-        sigma=std_devs(centers_of_mass), bounds=([0, -np.inf, 0], [np.inf, np.inf, np.inf]),
-        absolute_sigma=True,
-        p0=initial_parameter_guess)
-
-    residual = abs(sine_fit_function(np.array(nominal_spin_phase_angles), a, phi, b) - nominal_centers_of_mass)
-    reduced_chisq = np.sum(np.square(residual / std_devs(centers_of_mass))) / (len(spin_phase_angles) - 3)
-
-    phi = np.mod(phi, 360)
-
-    return correlated_values((a, phi, b), pcov), reduced_chisq
-
-
-def get_proton_peak_indices(count_rates):
-    return get_peak_indices(count_rates, 4)
-
-
-def interpolate_angle(center_of_mass_index, spin_angles):
-    spin_angle = np.interp(center_of_mass_index, np.arange(len(spin_angles)), np.unwrap(spin_angles, period=360))
-    return np.mod(spin_angle, 360)
-
-
-def calculate_proton_centers_of_mass(coincidence_count_rates, energies, epoch):
-    energies_at_center_of_mass = []
-    energies_at_center_of_mass_uncertainties = []
-
-    spin_angles_at_center_of_mass = []
-    for i in range(len(epoch)):
-        rates = coincidence_count_rates[i]
-        peak_slice = get_proton_peak_indices(rates)
-        center_of_mass_index = find_peak_center_of_mass_index(peak_slice, rates, 13, 4)
-        energy_at_center_of_mass = interpolate_energy(center_of_mass_index, energies[i])
-
-        measurement_interval = 1 / 6 * ONE_SECOND_IN_NANOSECONDS
-        index_into_full_sweep = (center_of_mass_index + 1)
-        time_at_center_of_mass = epoch[i] + measurement_interval * index_into_full_sweep + measurement_interval / 2
-        spin_angle = get_angle(time_at_center_of_mass)
-        energies_at_center_of_mass.append(energy_at_center_of_mass.nominal_value)
-        energies_at_center_of_mass_uncertainties.append(energy_at_center_of_mass.std_dev)
-        spin_angles_at_center_of_mass.append(spin_angle)
-
-    return uarray(energies_at_center_of_mass, energies_at_center_of_mass_uncertainties), spin_angles_at_center_of_mass
-
-
-def get_spin_angle_from_swapi_axis_in_despun_frame(instrument_axis: np.ndarray):
-    x, y, _ = instrument_axis
-    return np.mod(np.rad2deg(np.atan2(-1 * x, y)), 360)
-
-
-@wrap
-def get_angle(epoch) -> float:
-    rotation_matrix = spiceypy.pxform("IMAP_SWAPI", "IMAP_DPS",
-                                      spiceypy.unitim(epoch / ONE_SECOND_IN_NANOSECONDS, "TT", "ET"))
-    swapi_instrument_axis_in_despun_imap_frame = rotation_matrix @ np.array([0, 0, -1])
-    return get_spin_angle_from_swapi_axis_in_despun_frame(swapi_instrument_axis_in_despun_imap_frame)
-
-
-def calculate_sw_speed(particle_mass, particle_charge, energy):
-    if np.size(energy) == 0:
-        return np.array([])
-    dimensions = np.asanyarray(energy).ndim
-    if dimensions > 0:
-        if isinstance(np.ravel(energy)[0], uncertainties.UFloat):
-            return unumpy.sqrt(2 * energy * particle_charge / particle_mass) / METERS_PER_KILOMETER
-        return np.sqrt(2 * energy * particle_charge / particle_mass) / METERS_PER_KILOMETER
-    else:
-        return umath.sqrt(2 * energy * particle_charge / particle_mass) / METERS_PER_KILOMETER
-
-
-def calculate_sw_speed_h_plus(energy):
-    return calculate_sw_speed(PROTON_MASS_KG, PROTON_CHARGE_COULOMBS, energy)
-
-
-def calculate_proton_solar_wind_speed(coincidence_count_rates, energies, epoch):
-    energies = extract_coarse_sweep(energies)
-    coincidence_count_rates = extract_coarse_sweep(coincidence_count_rates)
-
-    energies_at_center_of_mass, spin_angles_at_center_of_mass = calculate_proton_centers_of_mass(
-        coincidence_count_rates, energies, epoch)
-
-    (a, phi, b), chi_sq = fit_energy_per_charge_peak_variations(energies_at_center_of_mass,
-                                                                spin_angles_at_center_of_mass)
-
-    proton_sw_speed = calculate_sw_speed_h_plus(b)
-    return proton_sw_speed, a, phi, b, chi_sq
-
-
-def estimate_deflection_and_clock_angles(speed) -> tuple[float, float]:
-    orbital_velocity = 29.78
-    spin_axis_offset = 4
-    raw_deflection = np.rad2deg(np.arcsin(orbital_velocity / speed)) - spin_axis_offset
-
-    if raw_deflection > 0:
-        return raw_deflection, 270
-    else:
-        return -raw_deflection, 90
diff --git a/imap_l3_processing/swapi/l3a/science/calculate_proton_solar_wind_temperature_and_density.py b/imap_l3_processing/swapi/l3a/science/calculate_proton_solar_wind_temperature_and_density.py
deleted file mode 100644
index 2a0b94b49..000000000
--- a/imap_l3_processing/swapi/l3a/science/calculate_proton_solar_wind_temperature_and_density.py
+++ /dev/null
@@ -1,206 +0,0 @@
-from dataclasses import dataclass, astuple
-from typing import Callable
-
-import numpy as np
-import scipy
-import uncertainties
-from matplotlib import pyplot as plt
-from numpy import ndarray
-from scipy.interpolate import LinearNDInterpolator, interp1d
-from scipy.special import erf
-from uncertainties import correlated_values, ufloat
-from uncertainties.unumpy import uarray, nominal_values, std_devs
-
-from imap_l3_processing import constants
-from imap_l3_processing.constants import PROTON_MASS_KG, BOLTZMANN_CONSTANT_JOULES_PER_KELVIN, METERS_PER_KILOMETER, \
-    CENTIMETERS_PER_METER, SWAPI_EFFECTIVE_AREA_CM2
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_speed import get_proton_peak_indices, \
-    calculate_sw_speed_h_plus
-from imap_l3_processing.swapi.l3a.science.speed_calculation import find_peak_center_of_mass_index, interpolate_energy, \
-    extract_coarse_sweep
-from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
-
-
-def proton_count_rate_model(efficiency, ev_per_q, density_per_cm3, temperature, bulk_flow_speed_km_per_s):
-    density_per_m3 = density_per_cm3 * CENTIMETERS_PER_METER ** 3
-    bulk_flow_speed_meters_per_s = bulk_flow_speed_km_per_s * METERS_PER_KILOMETER
-    energy = ev_per_q * constants.PROTON_CHARGE_COULOMBS
-    k = BOLTZMANN_CONSTANT_JOULES_PER_KELVIN
-    a_eff_cm2 = efficiency * SWAPI_EFFECTIVE_AREA_CM2
-    a_eff_m2 = a_eff_cm2 / CENTIMETERS_PER_METER ** 2
-
-    delta_e_over_e = 0.085
-    delta_v_over_v = 1 / 2 * delta_e_over_e
-    delta_phi_degrees = 30
-
-    m = PROTON_MASS_KG
-    v_th = np.sqrt(2 * k * temperature / m)
-    beta = 1 / v_th ** 2
-    v_e = np.sqrt(2 * energy / m)
-    result = (density_per_m3 * a_eff_m2 * (beta / np.pi) ** (3 / 2)
-              * np.exp(-beta * (v_e ** 2 + bulk_flow_speed_meters_per_s ** 2 - 2 * v_e * bulk_flow_speed_meters_per_s))
-              * np.sqrt(np.pi / (beta * bulk_flow_speed_meters_per_s * v_e))
-              * erf(np.sqrt(beta * bulk_flow_speed_meters_per_s * v_e) * np.radians(delta_phi_degrees / 2))
-              * v_e ** 4 * delta_v_over_v * np.arcsin(v_th / v_e))
-
-    return result
-
-
-def calculate_proton_solar_wind_temperature_and_density_for_one_sweep(coincident_count_rates: uarray, energy: ndarray,
-                                                                      efficiency: float):
-    coincident_count_rates = extract_coarse_sweep(coincident_count_rates)
-    energy = extract_coarse_sweep(energy)
-    proton_peak_indices = get_proton_peak_indices(coincident_count_rates)
-
-    initial_speed_guess = calculate_proton_speed_from_one_sweep(coincident_count_rates, energy, proton_peak_indices)
-
-    peak_energies = energy[proton_peak_indices]
-    peak_count_rates = coincident_count_rates[proton_peak_indices]
-    at_least_minimum = nominal_values(peak_count_rates) >= 13
-    filtered_peak_count_rates = peak_count_rates[at_least_minimum]
-    peak_energies_filtered = peak_energies[at_least_minimum]
-
-    initial_parameter_guess = [5, 1e5]
-
-    def model(ev_per_q: ndarray, density: float, temperature: float):
-        return proton_count_rate_model(efficiency, ev_per_q, density, temperature, nominal_values(initial_speed_guess))
-
-    values, covariance = scipy.optimize.curve_fit(model,
-                                                  peak_energies_filtered,
-                                                  nominal_values(filtered_peak_count_rates),
-                                                  sigma=std_devs(filtered_peak_count_rates),
-                                                  absolute_sigma=True,
-                                                  bounds=[[0, 0], [np.inf, np.inf]],
-                                                  p0=initial_parameter_guess)
-    residual = abs(model(peak_energies_filtered, *values) - nominal_values(filtered_peak_count_rates))
-    reduced_chisq = np.sum(np.square(residual / std_devs(filtered_peak_count_rates))) / (
-            len(peak_energies_filtered) - 2)
-
-    bad_fit_flag = SwapiL3Flags.NONE
-    if reduced_chisq > 10:
-        bad_fit_flag = SwapiL3Flags.HI_CHI_SQ
-
-    density, temperature = correlated_values(values, covariance)
-
-    return ProtonSolarWindTemperatureAndDensity(temperature, density, bad_fit_flag)
-
-
-def calculate_uncalibrated_proton_solar_wind_temperature_and_density(coincident_count_rates: uarray, energy: ndarray,
-                                                                     efficiency: float):
-    temperatures_per_sweep = []
-    densities_per_sweep = []
-    bad_temperatures = []
-    bad_densities = []
-
-    for sweep, single_energy in zip(coincident_count_rates, energy):
-        try:
-            proton_temp_density = calculate_proton_solar_wind_temperature_and_density_for_one_sweep(sweep,
-                                                                                                    single_energy,
-                                                                                                    efficiency)
-            if proton_temp_density.bad_fit_flag == SwapiL3Flags.HI_CHI_SQ:
-                bad_temperatures.append(proton_temp_density.temperature)
-                bad_densities.append(proton_temp_density.density)
-            elif proton_temp_density.bad_fit_flag == SwapiL3Flags.NONE:
-                temperatures_per_sweep.append(proton_temp_density.temperature)
-                densities_per_sweep.append(proton_temp_density.density)
-        except ValueError as e:
-            continue
-    if len(temperatures_per_sweep) >= 3:
-        bad_fit_flag = SwapiL3Flags.NONE
-        average_temp = np.average(temperatures_per_sweep, weights=1 / std_devs(temperatures_per_sweep) ** 2)
-        average_density = np.average(densities_per_sweep, weights=1 / std_devs(densities_per_sweep) ** 2)
-    else:
-        bad_fit_flag = SwapiL3Flags.HI_CHI_SQ
-        temperatures = temperatures_per_sweep + bad_temperatures
-        densities = densities_per_sweep + bad_densities
-
-        average_temp = np.average(temperatures,
-                                  weights=1 / std_devs(temperatures) ** 2)
-        average_density = np.average(densities,
-                                     weights=1 / std_devs(densities) ** 2)
-
-    return ProtonSolarWindTemperatureAndDensity(average_temp, average_density, bad_fit_flag)
-
-
-def calculate_proton_speed_from_one_sweep(coincident_count_rates, energy, proton_peak_indices):
-    center_of_mass_index = find_peak_center_of_mass_index(proton_peak_indices, coincident_count_rates,
-                                                          minimum_count_rate=13)
-    energy_at_com = interpolate_energy(center_of_mass_index, energy)
-    initial_speed_guess = calculate_sw_speed_h_plus(energy_at_com)
-    return initial_speed_guess
-
-
-class ProtonTemperatureAndDensityCalibrationTable:
-    def __init__(self, lookup_table_array: ndarray):
-        self.proton_sw_speed = np.unique(lookup_table_array[:, 0])
-        self.deflection_angle = np.unique(lookup_table_array[:, 1])
-        self.clock_angle = np.unique(lookup_table_array[:, 2])
-
-        assert np.all(np.sort(self.proton_sw_speed) == self.proton_sw_speed)
-        assert np.all(np.sort(self.deflection_angle) == self.deflection_angle)
-        assert np.all(np.sort(self.clock_angle) == self.clock_angle)
-
-        values_shape = (len(self.proton_sw_speed), len(self.deflection_angle), len(self.clock_angle))
-        self.reshaped_lookup = lookup_table_array.reshape(*values_shape, -1, 7)
-
-        if 360.0 not in self.clock_angle:
-            assert self.clock_angle[0] == 0.0, "expected table to start at angle 0"
-            self.clock_angle = np.append(self.clock_angle, 360.0)
-            self.reshaped_lookup = np.append(self.reshaped_lookup, self.reshaped_lookup[:, :, 0:1], axis=2)
-
-    @uncertainties.wrap
-    def calibrate_density(self, solar_wind_speed, deflection_angle, clock_angle, fit_density, fit_temperature):
-        interp = self.build_interpolator(solar_wind_speed, deflection_angle, clock_angle % 360)
-        return interp(fit_density, fit_temperature)[0]
-
-    @uncertainties.wrap
-    def calibrate_temperature(self, solar_wind_speed, deflection_angle, clock_angle, fit_density, fit_temperature):
-        interp = self.build_interpolator(solar_wind_speed, deflection_angle, clock_angle % 360)
-        return interp(fit_density, fit_temperature)[1]
-
-    def build_interpolator(self, solar_wind_speed, deflection_angle, clock_angle) -> Callable:
-        v = self.reshaped_lookup
-        v1 = interp1d(self.proton_sw_speed, v, axis=0)(solar_wind_speed)
-        v2 = interp1d(self.deflection_angle, v1, axis=0)(deflection_angle)
-        v3 = interp1d(self.clock_angle, v2, axis=0)(clock_angle)
-
-        coords = v3[:, [3, 5]]
-        values = v3[:, [4, 6]]
-        return LinearNDInterpolator(coords, values, rescale=True)
-
-    @classmethod
-    def from_file(cls, file_path):
-        lookup_table_array = np.loadtxt(file_path)
-        return cls(lookup_table_array)
-
-
-def calculate_proton_solar_wind_temperature_and_density(lookup_table: ProtonTemperatureAndDensityCalibrationTable,
-                                                        proton_solar_wind_speed, deflection_angle,
-                                                        clock_angle, coincident_count_rates: uarray, energy: ndarray,
-                                                        efficiency: float):
-    temperature, density, bad_fit_flag = astuple(calculate_uncalibrated_proton_solar_wind_temperature_and_density(
-        coincident_count_rates,
-        energy, efficiency))
-    calibrated_temperature = lookup_table.calibrate_temperature(proton_solar_wind_speed, deflection_angle, clock_angle,
-                                                                density,
-                                                                temperature)
-    calibrated_density = lookup_table.calibrate_density(proton_solar_wind_speed, deflection_angle, clock_angle,
-                                                        density,
-                                                        temperature)
-    return ProtonSolarWindTemperatureAndDensity(calibrated_temperature, calibrated_density, bad_fit_flag)
-
-
-def demo(density=5.0, temp=1e5, speed=450):
-    voltage = np.geomspace(100, 19000, 62)
-
-    plt.loglog(voltage, proton_count_rate_model(voltage, density, temp, speed))
-    plt.ylim(1e-3, 1e8)
-
-    plt.show()
-
-
-@dataclass
-class ProtonSolarWindTemperatureAndDensity:
-    temperature: ufloat
-    density: ufloat
-    bad_fit_flag: int = SwapiL3Flags.NONE
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/__init__.py b/imap_l3_processing/swapi/l3a/science/solar_wind/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/__init__.py b/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/calculate_alpha_solar_wind_moments.py b/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/calculate_alpha_solar_wind_moments.py
new file mode 100644
index 000000000..4f495ba51
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/calculate_alpha_solar_wind_moments.py
@@ -0,0 +1,315 @@
+from dataclasses import dataclass
+from typing import Optional
+
+import numpy as np
+import scipy.optimize
+from numpy import ndarray
+from uncertainties import UFloat, covariance_matrix, ufloat
+
+from imap_l3_processing.constants import (
+    ALPHA_MASS_PER_CHARGE_M_P_PER_E,
+    ALPHA_PARTICLE_MASS_KG,
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_model import (
+    ProtonSolarWindFitResult,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    model_solar_wind_ideal_coincidence_rates,
+)
+from imap_l3_processing.swapi.response.deadtime import deadtime_factor
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    SolarWindFitContext,
+    build_solar_wind_fit_context,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    LOG_DENSITY_IDX,
+    LOG_TEMPERATURE_IDX,
+    SolarWindParams,
+    VELOCITY_SLICE,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.uncertainties import (
+    compute_hc3_parameter_covariance,
+    make_correlated_velocity,
+    r_squared,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.alpha.utils import (
+    get_alpha_peak_indices,
+)
+from imap_l3_processing.swapi.constants import (
+    SWAPI_K_FACTOR,
+)
+from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
+from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
+
+
+@dataclass
+class AlphaSolarWindMoments:
+    density: UFloat  # cm^-3
+    temperature: UFloat  # K
+    bulk_velocity_rtn: tuple[UFloat, UFloat, UFloat]  # km/s, [R, T, N]; correlated
+    delta_v: UFloat  # km/s, signed; +Δv ⇔ alpha drifts along +B̂ vs proton frame
+    bad_fit_flag: int
+
+    def bulk_velocity_rtn_nominal(self) -> ndarray:
+        """Nominal RTN velocity vector (km/s); shape (3,)."""
+        return np.array([v.nominal_value for v in self.bulk_velocity_rtn])
+
+    def bulk_velocity_rtn_covariance(self) -> ndarray:
+        """3×3 RTN velocity covariance (km²/s²); = Σ_vp + σ_Δv² B̂B̂ᵀ."""
+        return np.array(covariance_matrix(self.bulk_velocity_rtn))
+
+
+def _nan_alpha_moments(flag: int) -> AlphaSolarWindMoments:
+    nan = ufloat(np.nan, np.nan)
+    return AlphaSolarWindMoments(
+        density=nan,
+        temperature=nan,
+        bulk_velocity_rtn=(nan, nan, nan),
+        delta_v=nan,
+        bad_fit_flag=int(flag),
+    )
+
+
+class _AlphaEvaluator:
+    def __init__(
+        self,
+        proton_bulk: ndarray,
+        magnetic_field_direction: ndarray,
+        proton_true_rate: ndarray,
+        alpha_ctx: SolarWindFitContext,
+    ):
+        self.proton_bulk = proton_bulk
+        self.magnetic_field_direction = magnetic_field_direction
+        self.proton_true_rate = proton_true_rate
+        self.alpha_ctx = alpha_ctx
+        self._last_state: Optional[ndarray] = None
+        self._last_residuals: Optional[ndarray] = None
+        self._last_jacobian: Optional[ndarray] = None
+
+    def _eval(self, x: ndarray) -> None:
+        n_a = float(np.exp(x[0]))
+        T_a = float(np.exp(x[1]))
+        delta_v = float(x[2])
+        v_a_rtn = self.proton_bulk + delta_v * self.magnetic_field_direction
+        alpha_true, jacobian_alpha_5d = model_solar_wind_ideal_coincidence_rates(
+            SolarWindParams(n_a, v_a_rtn, T_a, self.alpha_ctx.mass_kg),
+            self.alpha_ctx,
+        )
+        total_true = self.proton_true_rate + alpha_true
+        deadtime = deadtime_factor(total_true)
+        residuals = total_true * deadtime - self.alpha_ctx.count_rate.ravel()
+
+        deadtime_squared = deadtime * deadtime
+        jacobian = np.empty((alpha_true.size, 3))
+        jacobian[:, 0] = deadtime_squared * jacobian_alpha_5d[:, LOG_DENSITY_IDX]
+        jacobian[:, 1] = deadtime_squared * jacobian_alpha_5d[:, LOG_TEMPERATURE_IDX]
+        jacobian[:, 2] = deadtime_squared * (
+            jacobian_alpha_5d[:, VELOCITY_SLICE] @ self.magnetic_field_direction
+        )
+
+        self._last_state = x.copy()
+        self._last_residuals = residuals
+        self._last_jacobian = jacobian
+
+    def _refresh(self, x: ndarray) -> None:
+        if self._last_state is None or not np.array_equal(x, self._last_state):
+            self._eval(x)
+
+    def residuals(self, x: ndarray) -> ndarray:
+        self._refresh(x)
+        return self._last_residuals
+
+    def jacobian(self, x: ndarray) -> ndarray:
+        self._refresh(x)
+        return self._last_jacobian
+
+
+def fit_solar_wind_alpha_moments(
+    count_rate: ndarray,
+    esa_voltage: ndarray,
+    measurement_time: ndarray,
+    swapi_response: SwapiResponse,
+    proton_moments: ProtonSolarWindFitResult,
+    magnetic_field_direction: ndarray,
+    alpha_effective_area_scale: float,
+    proton_effective_area_scale: float,
+    rotation_matrices: Optional[ndarray] = None,
+) -> AlphaSolarWindMoments:
+    proton_bad_fit_flag = int(proton_moments.bad_fit_flag)
+    proton_bulk_rtn = proton_moments.bulk_velocity_rtn_nominal()
+
+    if not np.all(np.isfinite(proton_bulk_rtn)):
+        return _nan_alpha_moments(proton_bad_fit_flag)
+
+    bad_fit_flag = proton_bad_fit_flag
+
+    magnetic_field_direction = np.asarray(magnetic_field_direction, dtype=float)
+    if not np.all(np.isfinite(magnetic_field_direction)):
+        return _nan_alpha_moments(bad_fit_flag)
+
+    if rotation_matrices is None:
+        from imap_l3_processing.swapi.l3a.utils import get_swapi_geometry
+
+        rotation_matrices = get_swapi_geometry(measurement_time)
+
+    proton_ctx = build_solar_wind_fit_context(
+        count_rate=count_rate,
+        esa_voltage=esa_voltage,
+        swapi_response=swapi_response,
+        central_effective_area_scale=proton_effective_area_scale,
+        rotation_matrices=rotation_matrices,
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    )
+    alpha_ctx = build_solar_wind_fit_context(
+        count_rate=count_rate,
+        esa_voltage=esa_voltage,
+        swapi_response=swapi_response,
+        central_effective_area_scale=alpha_effective_area_scale,
+        rotation_matrices=rotation_matrices,
+        mass_kg=ALPHA_PARTICLE_MASS_KG,
+        mass_per_charge_m_p_per_e=ALPHA_MASS_PER_CHARGE_M_P_PER_E,
+    )
+
+    proton_true_rate, _ = model_solar_wind_ideal_coincidence_rates(
+        SolarWindParams(
+            density=proton_moments.density.nominal_value,
+            bulk_velocity_rtn=proton_bulk_rtn,
+            temperature=proton_moments.temperature.nominal_value,
+            mass=proton_ctx.mass_kg,
+        ),
+        proton_ctx,
+    )
+
+    initial_guess = _alpha_initial_guess(
+        proton_true_rate=proton_true_rate,
+        proton_temperature=proton_moments.temperature.nominal_value,
+        alpha_ctx=alpha_ctx,
+        proton_bulk_velocity_rtn=proton_bulk_rtn,
+        magnetic_field_direction=magnetic_field_direction,
+    )
+    if initial_guess is None:
+        return _nan_alpha_moments(bad_fit_flag | SwapiL3Flags.FIT_ERROR)
+
+    n0, T0, dv0, peak_bin_idx = initial_guess
+    proton_bulk = proton_bulk_rtn
+
+    n_sweeps, n_bins = alpha_ctx.count_rate.shape
+    count_rate_flat = count_rate.ravel()
+    peak_flat_idx = np.concatenate([peak_bin_idx + s * n_bins for s in range(n_sweeps)])
+    count_rate_peak = count_rate_flat[peak_flat_idx]
+    keep = count_rate_peak > 0
+    if not np.all(keep):
+        peak_flat_idx = peak_flat_idx[keep]
+    proton_true_rate_peak = proton_true_rate[peak_flat_idx]
+    alpha_ctx_peak = alpha_ctx.subset(peak_flat_idx)
+
+    evaluator = _AlphaEvaluator(
+        proton_bulk=proton_bulk,
+        magnetic_field_direction=magnetic_field_direction,
+        proton_true_rate=proton_true_rate_peak,
+        alpha_ctx=alpha_ctx_peak,
+    )
+
+    x0 = np.array([np.log(max(n0, 1e-3)), np.log(max(T0, 1e-3)), dv0])
+    result = scipy.optimize.least_squares(
+        evaluator.residuals, x0, jac=evaluator.jacobian, method="lm"
+    )
+
+    n_a_fit = float(np.exp(result.x[0]))
+    T_a_fit = float(np.exp(result.x[1]))
+    dv_fit = float(result.x[2])
+    bulk_velocity_rtn = proton_bulk + dv_fit * magnetic_field_direction
+
+    if not result.success:
+        return _nan_alpha_moments(bad_fit_flag | SwapiL3Flags.FIT_ERROR)
+
+    n_peak_bins = peak_bin_idx.size
+    if result.fun.size == n_sweeps * n_peak_bins:
+        averaged_count_rate = np.nanmean(
+            alpha_ctx_peak.count_rate.reshape(n_sweeps, n_peak_bins), axis=0
+        )
+        averaged_residual = np.nanmean(
+            result.fun.reshape(n_sweeps, n_peak_bins), axis=0
+        )
+        fit_r_squared = r_squared(averaged_residual, averaged_count_rate)
+    else:
+        fit_r_squared = r_squared(result.fun, alpha_ctx_peak.count_rate)
+
+    if fit_r_squared < 0.9:
+        return _nan_alpha_moments(bad_fit_flag | SwapiL3Flags.BAD_FIT)
+
+    cov_x = compute_hc3_parameter_covariance(result.jac, result.fun)
+    density_sigma = float(n_a_fit * np.sqrt(max(cov_x[0, 0], 0.0)))
+    temperature_sigma = float(T_a_fit * np.sqrt(max(cov_x[1, 1], 0.0)))
+    delta_v_sigma = float(np.sqrt(max(cov_x[2, 2], 0.0)))
+
+    sigma_dv2 = max(cov_x[2, 2], 0.0)
+    velocity_covariance_rtn = (
+        proton_moments.bulk_velocity_rtn_covariance()
+        + sigma_dv2 * np.outer(magnetic_field_direction, magnetic_field_direction)
+    )
+
+    return AlphaSolarWindMoments(
+        density=ufloat(n_a_fit, density_sigma),
+        temperature=ufloat(T_a_fit, temperature_sigma),
+        bulk_velocity_rtn=make_correlated_velocity(
+            bulk_velocity_rtn, velocity_covariance_rtn
+        ),
+        delta_v=ufloat(dv_fit, delta_v_sigma),
+        bad_fit_flag=int(bad_fit_flag),
+    )
+
+
+def _alpha_initial_guess(
+    proton_true_rate: ndarray,
+    proton_temperature: float,
+    alpha_ctx: SolarWindFitContext,
+    proton_bulk_velocity_rtn: ndarray,
+    magnetic_field_direction: ndarray,
+) -> Optional[tuple]:
+    if alpha_ctx.count_rate.size == 0 or alpha_ctx.count_rate.ndim != 2:
+        return None
+
+    n_sweeps, n_bins = alpha_ctx.count_rate.shape
+    counts_per_sweep = alpha_ctx.count_rate
+    voltage_per_sweep = alpha_ctx.esa_voltage[0]
+    proton_true_per_sweep = proton_true_rate.reshape(n_sweeps, n_bins)
+    proton_obs_per_sweep = proton_true_per_sweep * deadtime_factor(
+        proton_true_per_sweep
+    )
+
+    count_avg = counts_per_sweep.mean(axis=0)
+    proton_bg_avg = proton_obs_per_sweep.mean(axis=0)
+    energies_per_sweep = SWAPI_K_FACTOR * np.abs(voltage_per_sweep)
+    proton_peak_index = np.argmax(proton_bg_avg)
+    residual = np.maximum(0, count_avg - proton_bg_avg * 2)
+
+    try:
+        peak = get_alpha_peak_indices(residual, energies_per_sweep, proton_peak_index)
+    except Exception:
+        return None
+
+    peak_idx = np.arange(peak.start, peak.stop)
+    if len(peak_idx) < 3:
+        return None
+    residual_peak = np.maximum(residual[peak_idx], 0.0)
+    if not np.any(residual_peak > 0):
+        return None
+
+    T_alpha = proton_temperature
+
+    unit_alpha, _ = model_solar_wind_ideal_coincidence_rates(
+        SolarWindParams(1.0, proton_bulk_velocity_rtn, T_alpha, alpha_ctx.mass_kg),
+        alpha_ctx,
+    )
+    unit_alpha_per_sweep = unit_alpha.reshape(n_sweeps, n_bins).mean(axis=0)
+    denom = float(np.nanmean(unit_alpha_per_sweep[peak_idx]))
+    if denom <= 0 or not np.isfinite(denom):
+        return None
+    n_alpha = float(np.nanmean(residual_peak)) / denom
+    n_alpha = max(n_alpha, 1e-3)
+
+    return (n_alpha, T_alpha, 0.0, peak_idx)
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/utils.py b/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/utils.py
new file mode 100644
index 000000000..f553d04e0
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/alpha/utils.py
@@ -0,0 +1,50 @@
+import numpy as np
+from numpy.typing import ArrayLike
+
+from imap_l3_processing.constants import (
+    ALPHA_PARTICLE_CHARGE_COULOMBS,
+    ALPHA_PARTICLE_MASS_KG,
+    METERS_PER_KILOMETER,
+)
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+
+
+def esa_voltage_to_alpha_speed(esa_voltage: ArrayLike) -> np.ndarray:
+    return (
+        np.sqrt(
+            2
+            * SWAPI_K_FACTOR
+            * ALPHA_PARTICLE_CHARGE_COULOMBS
+            * np.abs(esa_voltage)
+            / ALPHA_PARTICLE_MASS_KG
+        )
+        / METERS_PER_KILOMETER
+    )
+
+
+def get_alpha_peak_indices(residuals, energies, proton_peak_index) -> slice:
+    energies = np.asarray(energies)
+    assert np.all(energies[:-1] >= energies[1:]), "Energies must be decreasing"
+
+    min_energy = 1.5 * energies[proton_peak_index]
+    max_energy = 4.0 * energies[proton_peak_index]
+
+    def find_start_of_alpha_particle_peak():
+        start_bin = None
+        for i in reversed(range(proton_peak_index)):
+            if energies[i] >= min_energy:
+                start_bin = i
+                break
+        if start_bin is None:
+            return None
+        for i in reversed(range(start_bin + 1)):
+            if residuals[i] > residuals[i + 1] and residuals[i - 1] > residuals[i]:
+                return i
+        return None
+
+    start_of_alpha_peak = find_start_of_alpha_particle_peak()
+    if start_of_alpha_peak is None:
+        raise Exception("Alpha peak not found")
+
+    end_of_alpha_peak = np.searchsorted(-energies, -max_energy)
+    return slice(int(end_of_alpha_peak), start_of_alpha_peak + 1)
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/azimuthal_regions.py b/imap_l3_processing/swapi/l3a/science/solar_wind/azimuthal_regions.py
new file mode 100644
index 000000000..1d0d7a674
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/azimuthal_regions.py
@@ -0,0 +1,13 @@
+from typing import NamedTuple
+
+
+class Region(NamedTuple):
+    # exactly one of the two flags is True (see the REGION_* constants below)
+    is_sunglasses: bool
+    is_open_aperture: bool
+    azimuth_sign: int  # 0 for SG (centered on boresight), ±1 for OA halves
+
+
+REGION_SUNGLASSES = Region(True, False, 0)
+REGION_OPEN_APERTURE_NEG = Region(False, True, -1)
+REGION_OPEN_APERTURE_POS = Region(False, True, +1)
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/fit_context.py b/imap_l3_processing/swapi/l3a/science/solar_wind/fit_context.py
new file mode 100644
index 000000000..138a0540a
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/fit_context.py
@@ -0,0 +1,58 @@
+from typing import NamedTuple, Self
+
+import numba
+import numpy as np
+from numpy import ndarray
+
+from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
+
+
+class SolarWindFitContext(NamedTuple):
+    count_rate: ndarray
+    esa_voltage: ndarray
+    response_grids: numba.typed.List
+    rotation_matrices: ndarray
+    mass_kg: float
+
+    def subset(self, indices: ndarray) -> Self:
+        return self._replace(
+            count_rate=self.count_rate.ravel()[indices],
+            esa_voltage=self.esa_voltage.ravel()[indices],
+            response_grids=numba.typed.List([self.response_grids[i] for i in indices]),
+            rotation_matrices=self.rotation_matrices[indices],
+        )
+
+
+def build_solar_wind_fit_context(
+    count_rate: ndarray,
+    esa_voltage: ndarray,
+    swapi_response: SwapiResponse,
+    central_effective_area_scale: float,
+    rotation_matrices: ndarray,
+    mass_kg: float,
+    mass_per_charge_m_p_per_e: float,
+) -> SolarWindFitContext:
+    flat_voltage = esa_voltage.ravel()
+    keep = (flat_voltage > 0) & np.isfinite(flat_voltage)
+    if not np.all(keep):
+        esa_voltage = flat_voltage[keep]
+        count_rate = count_rate.ravel()[keep]
+        rotation_matrices = rotation_matrices[keep]
+        flat_voltage = esa_voltage
+
+    response_grids = numba.typed.List(
+        [
+            swapi_response.get_response_grid(
+                v, mass_per_charge_m_p_per_e, central_effective_area_scale
+            )
+            for v in flat_voltage
+        ]
+    )
+
+    return SolarWindFitContext(
+        count_rate=count_rate,
+        esa_voltage=esa_voltage,
+        response_grids=response_grids,
+        rotation_matrices=rotation_matrices,
+        mass_kg=float(mass_kg),
+    )
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/forward_model.py b/imap_l3_processing/swapi/l3a/science/solar_wind/forward_model.py
new file mode 100644
index 000000000..730c715f0
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/forward_model.py
@@ -0,0 +1,225 @@
+"""Solar-wind forward model and analytic Jacobian.
+
+Derivation in `docs/swapi/solar-wind-moments.md`."""
+
+import math
+
+import numba
+import numpy as np
+
+from imap_l3_processing.swapi.l3a.science.solar_wind.azimuthal_regions import (
+    REGION_OPEN_APERTURE_NEG,
+    REGION_OPEN_APERTURE_POS,
+    REGION_SUNGLASSES,
+    Region,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.integration_limits import (
+    AngularQuadrature,
+    get_angular_quadrature,
+    get_speed_quadrature,
+    speed_window_misses_passband,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.utils import (
+    count_rate_conversion_factor,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    SolarWindFitContext,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    LOG_DENSITY_IDX,
+    LOG_TEMPERATURE_IDX,
+    N_STATE,
+    SolarWindParams,
+    VELOCITY_SLICE,
+    bulk_speed,
+    thermal_speed,
+)
+from imap_l3_processing.swapi.response.swapi_response import ResponseGrid
+
+
+@numba.njit
+def model_solar_wind_ideal_coincidence_rates(
+    sw_params: SolarWindParams,
+    ctx: SolarWindFitContext,
+):
+    n = len(ctx.response_grids)
+    rates = np.empty(n)
+    jacobian = np.empty((n, N_STATE))
+    for i in range(n):
+        rates[i], jacobian[i] = calculate_integral(
+            sw_params, ctx.response_grids[i], ctx.rotation_matrices[i]
+        )
+    return rates, jacobian
+
+
+@numba.njit(fastmath=True)
+def calculate_integral(
+    sw_params: SolarWindParams,
+    response_grid: ResponseGrid,
+    rotation_matrix,
+):
+    jacobian_row = np.zeros(N_STATE)
+    if speed_window_misses_passband(sw_params, response_grid):
+        return 0.0, jacobian_row
+
+    rate = 0.0
+    sg_rate = 0.0
+    for region in [
+        REGION_SUNGLASSES,
+        REGION_OPEN_APERTURE_NEG,
+        REGION_OPEN_APERTURE_POS,
+    ]:
+        skip_region, angular_quadrature = get_angular_quadrature(
+            sw_params, response_grid, region, rotation_matrix, sg_rate
+        )
+        if not skip_region:
+            region_rate, region_jac = _integrate_region(
+                sw_params,
+                response_grid,
+                region,
+                angular_quadrature,
+                rotation_matrix,
+            )
+            rate += region_rate
+            jacobian_row += region_jac
+            if region.is_sunglasses:
+                sg_rate = region_rate
+    return rate, jacobian_row
+
+
+@numba.njit(fastmath=True)
+def _integrate_region(
+    sw_params: SolarWindParams,
+    response_grid: ResponseGrid,
+    region: Region,
+    angular_quadrature: AngularQuadrature,
+    rotation_xyz_to_rtn,
+):
+    bulk_velocity = sw_params.bulk_velocity_rtn
+    bulk_r = bulk_velocity[0]
+    bulk_t = bulk_velocity[1]
+    bulk_n = bulk_velocity[2]
+
+    elevation_rate = 0.0
+    elevation_log_temperature_jacobian = 0.0
+    elevation_jacobian_r = 0.0
+    elevation_jacobian_t = 0.0
+    elevation_jacobian_n = 0.0
+    for i_elevation in range(angular_quadrature.elevation_points.shape[0]):
+        skip_elevation, speed_quadrature = get_speed_quadrature(
+            sw_params,
+            response_grid,
+            region,
+            angular_quadrature.elevation_points[i_elevation],
+        )
+        if skip_elevation:
+            continue
+
+        azimuth_rate = 0.0
+        azimuth_log_temperature_jacobian = 0.0
+        azimuth_jacobian_r = 0.0
+        azimuth_jacobian_t = 0.0
+        azimuth_jacobian_n = 0.0
+        for i_azimuth in range(angular_quadrature.azimuth_points.shape[0]):
+            direction_r, direction_t, direction_n = _direction_in_rtn(
+                angular_quadrature, rotation_xyz_to_rtn, i_elevation, i_azimuth
+            )
+
+            bulk_velocity_along_direction = (
+                direction_r * bulk_r + direction_t * bulk_t + direction_n * bulk_n
+            )
+
+            rate_integral = 0.0
+            log_temperature_jacobian_integral = 0.0
+            velocity_along_direction_integral = 0.0
+            for i_speed in range(speed_quadrature.points.shape[0]):
+                speed = speed_quadrature.points[i_speed]
+                exponent = (
+                    speed ** 2
+                    + bulk_speed(sw_params) ** 2
+                    - 2 * speed * bulk_velocity_along_direction
+                ) * 1.0 / (2 * thermal_speed(sw_params) ** 2)
+                weighted_integrand = (
+                    speed_quadrature.weights[i_speed]
+                    * speed_quadrature.speed_cubed_times_passband[i_speed]
+                    * math.exp(-exponent)
+                )
+                rate_integral += weighted_integrand
+
+                log_temperature_jacobian_integral += weighted_integrand * exponent
+                velocity_along_direction_integral += weighted_integrand * speed
+
+            azimuth_weight = (
+                angular_quadrature.azimuth_weights[i_azimuth]
+                * angular_quadrature.transmission_azimuth[i_azimuth]
+            )
+
+            azimuth_rate += azimuth_weight * rate_integral
+
+            azimuth_log_temperature_jacobian += (
+                azimuth_weight * log_temperature_jacobian_integral
+            )
+            azimuth_jacobian_r += azimuth_weight * (
+                direction_r * velocity_along_direction_integral - bulk_r * rate_integral
+            )
+            azimuth_jacobian_t += azimuth_weight * (
+                direction_t * velocity_along_direction_integral - bulk_t * rate_integral
+            )
+            azimuth_jacobian_n += azimuth_weight * (
+                direction_n * velocity_along_direction_integral - bulk_n * rate_integral
+            )
+
+        elevation_weight = (
+            angular_quadrature.elevation_weights[i_elevation]
+            * angular_quadrature.cos_elevation[i_elevation]
+        )
+        elevation_rate += azimuth_rate * elevation_weight
+        elevation_log_temperature_jacobian += (
+            azimuth_log_temperature_jacobian * elevation_weight
+        )
+        elevation_jacobian_r += azimuth_jacobian_r * elevation_weight
+        elevation_jacobian_t += azimuth_jacobian_t * elevation_weight
+        elevation_jacobian_n += azimuth_jacobian_n * elevation_weight
+
+    count_rate_factor = count_rate_conversion_factor(sw_params, response_grid)
+    rate = elevation_rate * count_rate_factor
+
+    jacobian = np.empty(N_STATE)
+    jacobian[LOG_DENSITY_IDX] = rate
+    jacobian[LOG_TEMPERATURE_IDX] = (
+        elevation_log_temperature_jacobian * count_rate_factor - 1.5 * rate
+    )
+    jacobian[VELOCITY_SLICE.start] = (
+            elevation_jacobian_r * count_rate_factor / thermal_speed(sw_params) ** 2
+    )
+    jacobian[VELOCITY_SLICE.start + 1] = (
+            elevation_jacobian_t * count_rate_factor / thermal_speed(sw_params) ** 2
+    )
+    jacobian[VELOCITY_SLICE.start + 2] = (
+            elevation_jacobian_n * count_rate_factor / thermal_speed(sw_params) ** 2
+    )
+
+    return rate, jacobian
+
+
+@numba.njit(fastmath=True)
+def _direction_in_rtn(angular_quadrature, rotation_xyz_to_rtn, i_elevation, i_azimuth):
+    cos_elevation = angular_quadrature.cos_elevation[i_elevation]
+    sin_elevation = angular_quadrature.sin_elevation[i_elevation]
+    sin_azimuth = angular_quadrature.sin_azimuth[i_azimuth]
+    cos_azimuth = angular_quadrature.cos_azimuth[i_azimuth]
+    direction_x = -cos_elevation * sin_azimuth
+    direction_y = -cos_elevation * cos_azimuth
+    direction_z = -sin_elevation
+    return (
+        rotation_xyz_to_rtn[0, 0] * direction_x
+        + rotation_xyz_to_rtn[0, 1] * direction_y
+        + rotation_xyz_to_rtn[0, 2] * direction_z,
+        rotation_xyz_to_rtn[1, 0] * direction_x
+        + rotation_xyz_to_rtn[1, 1] * direction_y
+        + rotation_xyz_to_rtn[1, 2] * direction_z,
+        rotation_xyz_to_rtn[2, 0] * direction_x
+        + rotation_xyz_to_rtn[2, 1] * direction_y
+        + rotation_xyz_to_rtn[2, 2] * direction_z,
+    )
+
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/integration_limits.py b/imap_l3_processing/swapi/l3a/science/solar_wind/integration_limits.py
new file mode 100644
index 000000000..f43a247ca
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/integration_limits.py
@@ -0,0 +1,216 @@
+from typing import NamedTuple
+
+import numba
+import numpy as np
+
+from imap_l3_processing.swapi.l3a.science.solar_wind.azimuthal_regions import (
+    Region,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.open_aperture_trimming import (
+    trim_oa_azimuth_by_integrand,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    SolarWindParams,
+    bulk_angles_in_instrument_frame,
+    bulk_speed,
+    thermal_speed,
+)
+from imap_l3_processing.swapi.response.azimuthal_transmission import (
+    interpolate_azimuthal_transmission,
+)
+from imap_l3_processing.swapi.response.passband_grid import (
+    interpolate_passband,
+    speed_ratio_range_at_elevation,
+)
+from imap_l3_processing.swapi.response.swapi_response import ResponseGrid
+
+
+class AngularQuadrature(NamedTuple):
+    elevation_points: np.ndarray
+    elevation_weights: np.ndarray
+    azimuth_points: np.ndarray
+    azimuth_weights: np.ndarray
+    sin_elevation: np.ndarray
+    cos_elevation: np.ndarray
+    sin_azimuth: np.ndarray
+    cos_azimuth: np.ndarray
+    transmission_azimuth: np.ndarray
+
+
+class SpeedQuadrature(NamedTuple):
+    points: np.ndarray
+    weights: np.ndarray
+    # per-node `v³ × passband(v/central_speed)`, normalized by the on-axis peak
+    speed_cubed_times_passband: np.ndarray
+
+
+_GL_NODES_ELEVATION, _GL_WEIGHTS_ELEVATION = np.polynomial.legendre.leggauss(21)
+_GL_NODES_AZIMUTH, _GL_WEIGHTS_AZIMUTH = np.polynomial.legendre.leggauss(21)
+_GL_NODES_SPEED, _GL_WEIGHTS_SPEED = np.polynomial.legendre.leggauss(15)
+
+
+EPSILON = 1e-6
+SPEED_HALF_WIDTH_VTH = 6.0
+
+
+@numba.njit
+def speed_window_misses_passband(
+    sw_params: SolarWindParams, response_grid: ResponseGrid
+) -> bool:
+    sigma = thermal_speed(sw_params)
+    speed = bulk_speed(sw_params)
+
+    sw_window_lo = speed - SPEED_HALF_WIDTH_VTH * sigma
+    sw_window_hi = speed + SPEED_HALF_WIDTH_VTH * sigma
+
+    # The passband grid is zero outside speed_ratio ∈ [0.9, 1.1] at every elevation
+    central_speed = response_grid.central_speed
+    passband_lo = central_speed * 0.9
+    passband_hi = central_speed * 1.1
+    return sw_window_hi < passband_lo or sw_window_lo > passband_hi
+
+
+@numba.njit
+def get_angular_quadrature(
+    sw_params: SolarWindParams,
+    response_grid: ResponseGrid,
+    region: Region,
+    rotation_matrix,
+    sg_rate: float,
+):
+    min_el, max_el, min_az, max_az = _angular_limits(
+        sw_params, rotation_matrix, region, response_grid
+    )
+    if max_el <= min_el or max_az <= min_az:
+        return True, None
+
+    if region.is_open_aperture:
+        min_az, max_az = trim_oa_azimuth_by_integrand(
+            response_grid,
+            sw_params,
+            rotation_matrix,
+            min_el,
+            max_el,
+            min_az,
+            max_az,
+            sg_rate,
+        )
+        if max_az <= min_az:
+            return True, None
+
+    def rescale(nodes, weights, lower, upper):
+        half = 0.5 * (upper - lower)
+        mid = 0.5 * (upper + lower)
+        return mid + half * nodes, half * weights
+
+    elevation_points, elevation_weights = rescale(
+        _GL_NODES_ELEVATION, _GL_WEIGHTS_ELEVATION, min_el, max_el
+    )
+    azimuth_points, azimuth_weights = rescale(
+        _GL_NODES_AZIMUTH, _GL_WEIGHTS_AZIMUTH, min_az, max_az
+    )
+    transmission_azimuth = np.array(
+        [
+            interpolate_azimuthal_transmission(
+                response_grid.azimuthal_transmission, az
+            )
+            for az in azimuth_points
+        ]
+    )
+    return False, AngularQuadrature(
+        elevation_points=elevation_points,
+        elevation_weights=elevation_weights,
+        azimuth_points=azimuth_points,
+        azimuth_weights=azimuth_weights,
+        sin_elevation=np.sin(np.radians(elevation_points)),
+        cos_elevation=np.cos(np.radians(elevation_points)),
+        sin_azimuth=np.sin(np.radians(azimuth_points)),
+        cos_azimuth=np.cos(np.radians(azimuth_points)),
+        transmission_azimuth=transmission_azimuth,
+    )
+
+
+@numba.njit
+def get_speed_quadrature(
+    sw_params: SolarWindParams,
+    response_grid: ResponseGrid,
+    region: Region,
+    elevation: float,
+):
+    passband = (
+        response_grid.sg_passband if region.is_sunglasses else response_grid.oa_passband
+    )
+    central_speed = response_grid.central_speed
+    sigma = thermal_speed(sw_params)
+    bulk_v = bulk_speed(sw_params)
+
+    # `bulk_v ± k·σ` Maxwellian window intersected with the region's passband at this elevation
+    ratio_lo, ratio_hi = speed_ratio_range_at_elevation(passband, elevation)
+    passband_lo = central_speed * ratio_lo
+    passband_hi = central_speed * ratio_hi
+    min_speed = max(bulk_v - SPEED_HALF_WIDTH_VTH * sigma, passband_lo)
+    max_speed = min(bulk_v + SPEED_HALF_WIDTH_VTH * sigma, passband_hi)
+    if max_speed <= min_speed:
+        return True, None
+
+    half = 0.5 * (max_speed - min_speed)
+    mid = 0.5 * (max_speed + min_speed)
+    points = mid + half * _GL_NODES_SPEED
+    weights = half * _GL_WEIGHTS_SPEED
+
+    speed_cubed_times_passband = np.empty_like(points)
+    for i, v in enumerate(points):
+        speed_cubed_times_passband[i] = v**3 * interpolate_passband(
+            passband, elevation, v / central_speed
+        )
+
+    return False, SpeedQuadrature(points, weights, speed_cubed_times_passband)
+
+
+@numba.njit
+def _angular_limits(
+    sw_params: SolarWindParams,
+    rotation_matrix,
+    region: Region,
+    response_grid: ResponseGrid,
+):
+    half_width = _maxwellian_angular_extent(
+        sw_params, response_grid.central_speed, EPSILON
+    )
+    bulk_az, bulk_el = bulk_angles_in_instrument_frame(sw_params, rotation_matrix)
+
+    if region.is_sunglasses:
+        el_lo, el_hi = response_grid.sg_passband.elevation_range
+        az_lo, az_hi = -20.0, 20.0
+    else:
+        el_lo, el_hi = response_grid.oa_passband.elevation_range
+        az_lo, az_hi = 20.0, 150.0
+        if region.azimuth_sign < 0:
+            az_lo, az_hi = -az_hi, -az_lo
+
+    min_el, max_el = _clamp_window(bulk_el, half_width, el_lo, el_hi)
+    min_az, max_az = _clamp_window(bulk_az, half_width, az_lo, az_hi)
+    return min_el, max_el, min_az, max_az
+
+
+@numba.njit
+def _maxwellian_angular_extent(sw_params, central_speed, epsilon):
+    sigma = thermal_speed(sw_params)
+    speed = bulk_speed(sw_params)
+    cos_theta = sigma**2 * np.log(epsilon) / (central_speed * speed) + 1
+    return float(np.degrees(np.arccos(_clamp(cos_theta, -1, +1))))
+
+
+@numba.njit
+def _clamp(x: float, lower: float, upper: float) -> float:
+    return min(max(x, lower), upper)
+
+
+@numba.njit
+def _clamp_window(
+    center: float, half_width: float, lower_bound: float, upper_bound: float
+):
+    return (
+        _clamp(center - half_width, lower_bound, upper_bound),
+        _clamp(center + half_width, lower_bound, upper_bound),
+    )
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/open_aperture_trimming.py b/imap_l3_processing/swapi/l3a/science/solar_wind/open_aperture_trimming.py
new file mode 100644
index 000000000..6a59a02bc
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/open_aperture_trimming.py
@@ -0,0 +1,131 @@
+import math
+
+import numba
+import numpy as np
+
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    SolarWindParams,
+    bulk_angles_in_instrument_frame,
+    bulk_speed, thermal_speed,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.utils import (
+    count_rate_conversion_factor,
+)
+from imap_l3_processing.swapi.response.azimuthal_transmission import (
+    interpolate_azimuthal_transmission,
+)
+from imap_l3_processing.swapi.response.passband_grid import (
+    speed_ratio_range_at_elevation,
+)
+from imap_l3_processing.swapi.response.swapi_response import ResponseGrid
+
+
+OA_SCAN_THRESHOLD = 1e-6
+OA_SCAN_RESOLUTION = 64
+OA_SKIP_FRACTION = 1e-3
+
+
+@numba.njit
+def trim_oa_azimuth_by_integrand(
+    response_grid: ResponseGrid,
+    sw_params: SolarWindParams,
+    rotation_matrix,
+    min_elevation: float,
+    max_elevation: float,
+    azimuth_lo: float,
+    azimuth_hi: float,
+    sg_rate: float,
+):
+    if azimuth_hi <= azimuth_lo:
+        return 0.0, 0.0
+
+    _, bulk_el = bulk_angles_in_instrument_frame(sw_params, rotation_matrix)
+    scan_elevation = min(max(bulk_el, min_elevation), max_elevation)
+    scan_azimuths = np.linspace(azimuth_lo, azimuth_hi, OA_SCAN_RESOLUTION)
+    transmission_x_maxwellian = _evaluate_oa_integrand_along_azimuth(
+        response_grid, sw_params, rotation_matrix, scan_azimuths, scan_elevation
+    )
+
+    threshold = OA_SCAN_THRESHOLD * np.max(transmission_x_maxwellian)
+    n = transmission_x_maxwellian.shape[0]
+    lower_index = 0
+    for i in range(n):
+        if transmission_x_maxwellian[i] > threshold:
+            lower_index = max(i - 1, 0)
+            break
+    upper_index = n - 1
+    for i in range(n - 1, -1, -1):
+        if transmission_x_maxwellian[i] > threshold:
+            upper_index = min(i + 1, n - 1)
+            break
+
+    dphi_deg = scan_azimuths[1] - scan_azimuths[0]
+    transmission_az_integral = float(np.trapezoid(
+        transmission_x_maxwellian[lower_index : upper_index + 1], dx=dphi_deg
+    ))
+    upper_bound = _oa_rate_upper_bound(
+        response_grid,
+        sw_params,
+        transmission_az_integral,
+        min_elevation,
+        max_elevation,
+    )
+    if upper_bound < max(0.1, OA_SKIP_FRACTION * sg_rate):
+        return 0.0, 0.0
+    return scan_azimuths[lower_index], scan_azimuths[upper_index]
+
+
+@numba.njit
+def _evaluate_oa_integrand_along_azimuth(
+    response_grid: ResponseGrid,
+    sw_params: SolarWindParams,
+    rotation_matrix,
+    scan_azimuths: np.ndarray,
+    scan_elevation: float,
+) -> np.ndarray:
+    sigma = thermal_speed(sw_params)
+    speed = bulk_speed(sw_params)
+    central_speed = response_grid.central_speed
+    bulk_az, bulk_el = bulk_angles_in_instrument_frame(sw_params, rotation_matrix)
+
+    sin_bulk_el = math.sin(np.radians(bulk_el))
+    cos_bulk_el = math.cos(np.radians(bulk_el))
+    sin_scan_el = math.sin(np.radians(scan_elevation))
+    cos_scan_el = math.cos(np.radians(scan_elevation))
+    cos_view_to_bulk = (
+        sin_bulk_el * sin_scan_el + cos_bulk_el * cos_scan_el
+        * np.cos(np.radians(scan_azimuths - bulk_az))
+    )
+    delta_v_sq = (
+        central_speed**2 + speed**2 - 2.0 * central_speed * speed * cos_view_to_bulk
+    )
+    maxwellian = np.exp(-delta_v_sq / (2.0 * sigma**2))
+
+    for i in range(scan_azimuths.shape[0]):
+        maxwellian[i] *= interpolate_azimuthal_transmission(
+            response_grid.azimuthal_transmission,
+            scan_azimuths[i],
+        )
+
+    return maxwellian
+
+
+@numba.njit
+def _oa_rate_upper_bound(
+    response_grid: ResponseGrid,
+    sw_params: SolarWindParams,
+    transmission_az_integral: float,
+    min_elevation: float,
+    max_elevation: float,
+) -> float:
+    central_speed = response_grid.central_speed
+    delta_theta_deg = max_elevation - min_elevation
+    ratio_lo, ratio_hi = speed_ratio_range_at_elevation(response_grid.oa_passband, 0.0)
+    delta_v = central_speed * (ratio_hi - ratio_lo)
+    return (
+            count_rate_conversion_factor(sw_params, response_grid)
+            * central_speed ** 3
+            * delta_theta_deg
+            * delta_v
+            * transmission_az_integral
+    )
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/optimizer.py b/imap_l3_processing/swapi/l3a/science/solar_wind/optimizer.py
new file mode 100644
index 000000000..ee3654c3c
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/optimizer.py
@@ -0,0 +1,88 @@
+from dataclasses import dataclass
+
+import numpy as np
+import scipy.optimize
+from numpy import ndarray
+
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    SolarWindFitContext,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    model_solar_wind_ideal_coincidence_rates,
+)
+from imap_l3_processing.swapi.response.deadtime import deadtime_factor
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
+
+
+@dataclass
+class OptimizeSolarWindParamsResult:
+    sw_params: SolarWindParams
+    residuals: ndarray  # count-rate residuals at the solution
+    jacobian: ndarray  # ∂residuals/∂state, columns ordered per the state vector
+    success: bool
+
+    @property
+    def mse(self) -> float:
+        return float(np.mean(self.residuals**2))
+
+
+def optimize_solar_wind_params(
+    initial_guess: SolarWindParams, ctx: SolarWindFitContext
+) -> OptimizeSolarWindParamsResult:
+    evaluator = _Evaluator(ctx)
+
+    raw: scipy.optimize.OptimizeResult = scipy.optimize.least_squares(
+        evaluator.residues,
+        initial_guess.to_vector(),
+        jac=evaluator.jacobian,
+        method="lm",
+        xtol=1e-4,
+        x_scale=1.0,
+    )
+
+    return OptimizeSolarWindParamsResult(
+        sw_params=SolarWindParams.from_vector(raw.x, ctx.mass_kg),
+        residuals=raw.fun,
+        jacobian=raw.jac,
+        success=bool(raw.success),
+    )
+
+
+class _Evaluator:
+    """Caches the most recent (residuals, jacobian) so scipy.least_squares' separate
+    `fun` and `jac` callbacks share a single forward-model evaluation per state."""
+
+    def __init__(self, ctx: SolarWindFitContext):
+        self.ctx = ctx
+        self._last_state: ndarray | None = None
+        self._last_resid: ndarray | None = None
+        self._last_jac: ndarray | None = None
+
+    def _eval(self, state: ndarray) -> None:
+        sw = SolarWindParams.from_vector(state, self.ctx.mass_kg)
+        rate_ideal, jacobian_ideal = model_solar_wind_ideal_coincidence_rates(
+            sw, self.ctx
+        )
+
+        df = deadtime_factor(rate_ideal)
+
+        rate_observable = rate_ideal * df
+        jacobian = jacobian_ideal * np.square(df)[:, np.newaxis]
+
+        residues = rate_observable - self.ctx.count_rate.ravel()
+
+        self._last_state = state.copy()
+        self._last_residues = residues
+        self._last_jacobian = jacobian
+
+    def _refresh(self, state: ndarray) -> None:
+        if self._last_state is None or not np.array_equal(state, self._last_state):
+            self._eval(state)
+
+    def residues(self, state: ndarray) -> ndarray:
+        self._refresh(state)
+        return self._last_residues
+
+    def jacobian(self, state: ndarray) -> ndarray:
+        self._refresh(state)
+        return self._last_jacobian
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/params.py b/imap_l3_processing/swapi/l3a/science/solar_wind/params.py
new file mode 100644
index 000000000..3f08e6eae
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/params.py
@@ -0,0 +1,83 @@
+import math
+from typing import NamedTuple
+
+import numba
+import numpy as np
+from numpy import ndarray
+
+from imap_l3_processing.constants import (
+    BOLTZMANN_CONSTANT_JOULES_PER_KELVIN,
+    METERS_PER_KILOMETER,
+)
+
+# layout of "x" vector for the least-squares optimizer
+LOG_DENSITY_IDX = 0
+LOG_TEMPERATURE_IDX = 1
+VELOCITY_SLICE = slice(2, 5)
+N_STATE = 5
+
+
+class SolarWindParams(NamedTuple):
+    density: float
+    bulk_velocity_rtn: ndarray  # shape (3,), km/s, inertial RTN
+    temperature: float  # K
+    mass: float  # kg
+
+    def to_vector(self) -> ndarray:
+        state = np.empty(N_STATE)
+        state[LOG_DENSITY_IDX] = math.log(self.density)
+        state[LOG_TEMPERATURE_IDX] = math.log(self.temperature)
+        state[VELOCITY_SLICE] = self.bulk_velocity_rtn
+        return state
+
+    @classmethod
+    def from_vector(cls, state: ndarray, mass: float) -> "SolarWindParams":
+        return cls(
+            density=math.exp(state[LOG_DENSITY_IDX]),
+            bulk_velocity_rtn=state[VELOCITY_SLICE],
+            temperature=math.exp(state[LOG_TEMPERATURE_IDX]),
+            mass=mass,
+        )
+
+
+@numba.njit
+def bulk_speed(sw_params: SolarWindParams) -> float:
+    """||bulk_velocity_rtn||, km/s."""
+    v = sw_params.bulk_velocity_rtn
+    return math.sqrt(v[0] ** 2 + v[1] ** 2 + v[2] ** 2)
+
+
+@numba.njit
+def bulk_angles_in_instrument_frame(sw_params: SolarWindParams, rotation_xyz_to_rtn):
+    v_xyz = rotation_xyz_to_rtn.T @ sw_params.bulk_velocity_rtn
+    return (
+        math.degrees(math.atan2(-v_xyz[0], -v_xyz[1])),
+        math.degrees(math.asin(-v_xyz[2] / bulk_speed(sw_params))),
+    )
+
+
+@numba.njit
+def thermal_speed(sw_params: SolarWindParams) -> float:
+    """Maxwellian standard deviation σ = √(kT/m), km/s."""
+    temperature_k = sw_params.temperature
+    mass_kg = sw_params.mass
+    return temperature_to_thermal_speed(mass_kg, temperature_k)
+
+
+@numba.njit
+def temperature_to_thermal_speed(mass: float, temperature: float) -> float:
+    """mass [kg], temperature [K] -> thermal speed [km/s]."""
+    return (
+        math.sqrt(BOLTZMANN_CONSTANT_JOULES_PER_KELVIN * temperature / mass)
+        / METERS_PER_KILOMETER
+    )
+
+
+@numba.njit
+def thermal_speed_to_temperature(thermal_speed: float, mass: float) -> float:
+    """thermal speed [km/s], mass [kg] -> temperature [K]."""
+    return (
+        mass
+        * (thermal_speed * METERS_PER_KILOMETER) ** 2
+        / BOLTZMANN_CONSTANT_JOULES_PER_KELVIN
+    )
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/proton/__init__.py b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/proton/basin_hopping.py b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/basin_hopping.py
new file mode 100644
index 000000000..ff49f76bc
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/basin_hopping.py
@@ -0,0 +1,90 @@
+import numpy as np
+from numpy import ndarray
+
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    SolarWindFitContext,
+)
+from imap_l3_processing.swapi.l3a.utils import optimal_density_scale
+from imap_l3_processing.swapi.l3a.science.solar_wind.utils import average_spin_axis_rtn
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    model_solar_wind_ideal_coincidence_rates,
+)
+from imap_l3_processing.swapi.response.deadtime import deadtime_factor
+from imap_l3_processing.swapi.l3a.science.solar_wind.optimizer import (
+    OptimizeSolarWindParamsResult,
+    optimize_solar_wind_params,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
+
+
+_MAX_BASIN_REFINE_ITERS = 6
+_ROTATED_RMSE_RATIO_THRESHOLD = 10
+
+
+def escape_local_minimum(
+    first_result: OptimizeSolarWindParamsResult,
+    ctx: SolarWindFitContext,
+) -> OptimizeSolarWindParamsResult:
+    spin_axis_rtn = average_spin_axis_rtn(ctx.rotation_matrices)
+
+    current_result = first_result
+    for _ in range(_MAX_BASIN_REFINE_ITERS):
+        flipped_mse, flipped_seed_params = _flipped_seed(
+            current_result, ctx, spin_axis_rtn
+        )
+
+        flipped_seed_is_substantially_worse_than_current = (
+            flipped_mse >= current_result.mse * _ROTATED_RMSE_RATIO_THRESHOLD**2
+        )
+        if flipped_seed_is_substantially_worse_than_current:
+            break
+
+        flipped_result: OptimizeSolarWindParamsResult = _restart_from_flipped_params(
+            flipped_seed_params, ctx
+        )
+
+        flipped_result_is_worse_than_current = flipped_result.mse > current_result.mse
+        if flipped_result_is_worse_than_current:
+            break
+
+        current_result = flipped_result
+
+    return current_result
+
+
+def _flipped_seed(
+    lm_result: OptimizeSolarWindParamsResult,
+    ctx: SolarWindFitContext,
+    spin_axis_rtn: ndarray,
+) -> tuple[float, SolarWindParams]:
+    sw = lm_result.sw_params
+    flipped_velocity = _flip_vector_about_axis(sw.bulk_velocity_rtn, spin_axis_rtn)
+    unit_ideal_rates, _ = model_solar_wind_ideal_coincidence_rates(
+        SolarWindParams(1.0, flipped_velocity, sw.temperature, sw.mass),
+        ctx,
+    )
+    count_rate_flat = ctx.count_rate.ravel()
+    flipped_density = optimal_density_scale(unit_ideal_rates, count_rate_flat)
+
+    # Same forward model as LM-1's residuals, evaluated self-consistently at
+    # `flipped_density` so the basin-check ratio compares apples to apples.
+    true_rate = flipped_density * unit_ideal_rates
+    obs_pred = true_rate * deadtime_factor(true_rate)
+    flipped_mse = float(np.mean((obs_pred - count_rate_flat) ** 2))
+
+    return flipped_mse, SolarWindParams(
+        density=flipped_density,
+        bulk_velocity_rtn=flipped_velocity,
+        temperature=sw.temperature,
+        mass=sw.mass,
+    )
+
+
+def _flip_vector_about_axis(v: ndarray, axis: ndarray) -> ndarray:
+    return 2.0 * axis * float(np.dot(axis, v)) - v
+
+
+def _restart_from_flipped_params(
+    flipped_params: SolarWindParams, ctx: SolarWindFitContext
+) -> OptimizeSolarWindParamsResult:
+    return optimize_solar_wind_params(flipped_params, ctx=ctx)
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/proton/fit_model.py b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/fit_model.py
new file mode 100644
index 000000000..e25e8d712
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/fit_model.py
@@ -0,0 +1,110 @@
+from dataclasses import dataclass
+
+import numpy as np
+from numpy import ndarray
+from uncertainties import UFloat, covariance_matrix, ufloat
+
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    SolarWindFitContext,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.initial_guess import (
+    calculate_initial_guess,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.optimizer import (
+    optimize_solar_wind_params,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.basin_hopping import (
+    escape_local_minimum,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.uncertainties import (
+    derive_uncertainties,
+    make_correlated_velocity,
+    r_squared,
+)
+from imap_l3_processing.swapi.constants import SWAPI_COARSE_SWEEP_BINS
+from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
+
+
+_R_SQUARED_PEAK_HALF_WIDTH = 4
+
+
+@dataclass
+class ProtonSolarWindFitResult:
+    density: UFloat  # cm^-3
+    temperature: UFloat  # K
+    bulk_velocity_rtn: tuple[UFloat, UFloat, UFloat]  # km/s, [R, T, N]; correlated
+    bad_fit_flag: int
+
+    def bulk_velocity_rtn_nominal(self) -> ndarray:
+        return np.array([v.nominal_value for v in self.bulk_velocity_rtn])
+
+    def bulk_velocity_rtn_covariance(self) -> ndarray:
+        return np.array(covariance_matrix(self.bulk_velocity_rtn))
+
+
+def fit_solar_wind_proton_model(ctx: SolarWindFitContext) -> ProtonSolarWindFitResult:
+    initial_guess = calculate_initial_guess(ctx)
+    first_result = optimize_solar_wind_params(initial_guess, ctx)
+    final_result = escape_local_minimum(first_result, ctx)
+    return _construct_fit_result(final_result, ctx)
+
+
+def _construct_fit_result(final_result, ctx):
+    if not final_result.success:
+        return _nan_proton_fit_result(int(SwapiL3Flags.FIT_ERROR))
+
+    fit_r_squared = _coarse_peak_averaged_r_squared(
+        final_result.residuals, ctx.count_rate
+    )
+    flag_bad = (
+        fit_r_squared < 0.9
+        or final_result.sw_params.temperature > 5.0e5
+    )
+    if flag_bad:
+        return _nan_proton_fit_result(int(SwapiL3Flags.BAD_FIT))
+
+    density_sigma, temperature_sigma, velocity_covariance = derive_uncertainties(
+        final_result, ctx
+    )
+    density = ufloat(final_result.sw_params.density, density_sigma)
+    temperature = ufloat(final_result.sw_params.temperature, temperature_sigma)
+    bulk_velocity_rtn = make_correlated_velocity(
+        final_result.sw_params.bulk_velocity_rtn, velocity_covariance
+    )
+    return ProtonSolarWindFitResult(
+        density=density,
+        temperature=temperature,
+        bulk_velocity_rtn=bulk_velocity_rtn,
+        bad_fit_flag=int(SwapiL3Flags.NONE),
+    )
+
+
+def _nan_proton_fit_result(bad_fit_flag: int) -> ProtonSolarWindFitResult:
+    nan = ufloat(np.nan, np.nan)
+    return ProtonSolarWindFitResult(
+        density=nan,
+        temperature=nan,
+        bulk_velocity_rtn=(nan, nan, nan),
+        bad_fit_flag=bad_fit_flag,
+    )
+
+
+def _coarse_peak_averaged_r_squared(residuals: ndarray, count_rate: ndarray) -> float:
+    if count_rate.ndim != 2:
+        return r_squared(residuals, count_rate)
+    n_coarse_bins = SWAPI_COARSE_SWEEP_BINS.stop - SWAPI_COARSE_SWEEP_BINS.start
+    coarse_count_rate_per_sweep = count_rate[:, :n_coarse_bins]
+    coarse_residual_per_sweep = residuals.reshape(count_rate.shape)[:, :n_coarse_bins]
+    averaged_count_rate = np.nanmean(coarse_count_rate_per_sweep, axis=0)
+    averaged_residual = np.nanmean(coarse_residual_per_sweep, axis=0)
+    peak_index = int(np.nanargmax(averaged_count_rate))
+    n_bins = averaged_count_rate.shape[0]
+    clamped_peak_index = max(
+        _R_SQUARED_PEAK_HALF_WIDTH,
+        min(peak_index, n_bins - _R_SQUARED_PEAK_HALF_WIDTH - 1),
+    )
+    window = slice(
+        clamped_peak_index - _R_SQUARED_PEAK_HALF_WIDTH,
+        clamped_peak_index + _R_SQUARED_PEAK_HALF_WIDTH + 1,
+    )
+    return r_squared(averaged_residual[window], averaged_count_rate[window])
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/proton/initial_guess.py b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/initial_guess.py
new file mode 100644
index 000000000..8f2617cc6
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/proton/initial_guess.py
@@ -0,0 +1,117 @@
+import numpy as np
+import scipy.optimize
+from numpy import ndarray
+from numpy.typing import ArrayLike
+
+from imap_l3_processing.constants import (
+    BOLTZMANN_CONSTANT_JOULES_PER_KELVIN,
+    METERS_PER_KILOMETER,
+    PROTON_CHARGE_COULOMBS,
+    PROTON_MASS_KG,
+)
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    model_solar_wind_ideal_coincidence_rates,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    SolarWindFitContext,
+)
+from imap_l3_processing.swapi.l3a.utils import optimal_density_scale
+from imap_l3_processing.swapi.l3a.science.solar_wind.utils import average_spin_axis_rtn
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams, temperature_to_thermal_speed, \
+    thermal_speed_to_temperature
+
+
+# 1 eV
+INITIAL_TEMPERATURE_FLOOR_K = (
+    PROTON_CHARGE_COULOMBS / BOLTZMANN_CONSTANT_JOULES_PER_KELVIN
+)
+
+
+def esa_voltage_to_proton_speed(esa_voltage: ArrayLike) -> np.ndarray:
+    return (
+        np.sqrt(
+            2
+            * SWAPI_K_FACTOR
+            * PROTON_CHARGE_COULOMBS
+            * np.abs(esa_voltage)
+            / PROTON_MASS_KG
+        )
+        / METERS_PER_KILOMETER
+    )
+
+
+def calculate_initial_guess(ctx: SolarWindFitContext) -> SolarWindParams:
+    count_rate = ctx.count_rate.ravel()
+    speed = esa_voltage_to_proton_speed(ctx.esa_voltage.ravel())
+
+    peak_idx = np.nanargmax(count_rate)
+    bulk_speed_seed = float(speed[peak_idx])
+
+    temperature_seed = max(
+        60000.0 * (bulk_speed_seed / 400.0) ** 2,
+        INITIAL_TEMPERATURE_FLOOR_K,
+    )
+
+    bulk_speed_init, temperature = _gaussian_refine_bulk_speed_and_temperature(
+        speed,
+        count_rate,
+        bulk_speed_seed,
+        temperature_seed,
+        ctx.mass_kg,
+    )
+
+    spin_axis_rtn = average_spin_axis_rtn(ctx.rotation_matrices)
+    bulk_velocity_rtn = -bulk_speed_init * spin_axis_rtn
+
+    unit_ideal_rates, _ = model_solar_wind_ideal_coincidence_rates(
+        SolarWindParams(1.0, bulk_velocity_rtn, temperature, ctx.mass_kg),
+        ctx,
+    )
+    density = optimal_density_scale(unit_ideal_rates, count_rate)
+
+    return SolarWindParams(
+        density=density,
+        bulk_velocity_rtn=bulk_velocity_rtn,
+        temperature=temperature,
+        mass=ctx.mass_kg,
+    )
+
+
+def _gaussian_refine_bulk_speed_and_temperature(
+    speed: ndarray,
+    count_rate: ndarray,
+    bulk_speed_seed: float,
+    temperature_seed: float,
+    mass_kg: float,
+) -> tuple[float, float]:
+    def gaussian(v, amplitude, mean, sigma):
+        return amplitude * np.exp(-0.5 * ((v - mean) / sigma) ** 2)
+
+    seed_normalization_coefficient = float(np.nanmax(count_rate))
+    seed_center = bulk_speed_seed
+    seed_scale = temperature_to_thermal_speed(mass_kg, temperature_seed)
+    p0 = [seed_normalization_coefficient, seed_center, seed_scale]
+
+    try:
+        (_, bulk_speed_fit, sigma_fit), _ = scipy.optimize.curve_fit(
+            gaussian,
+            speed,
+            count_rate,
+            p0=p0,
+        )
+    except RuntimeError as e:
+        raise RuntimeError(
+            f"Initial-guess Gaussian fit failed for spectrum with peak-bin "
+            f"speed {bulk_speed_seed:.1f} km/s and seed temperature "
+            f"{temperature_seed:.0f} K."
+        ) from e
+
+    sigma_fit = abs(float(sigma_fit))
+    temperature_fit = max(
+        thermal_speed_to_temperature(sigma_fit, mass_kg),
+        INITIAL_TEMPERATURE_FLOOR_K,
+    )
+    return float(bulk_speed_fit), float(temperature_fit)
+
+
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/uncertainties.py b/imap_l3_processing/swapi/l3a/science/solar_wind/uncertainties.py
new file mode 100644
index 000000000..aa2406f15
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/uncertainties.py
@@ -0,0 +1,87 @@
+import numpy as np
+from numpy import ndarray
+from uncertainties import UFloat, correlated_values, ufloat
+
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    SolarWindFitContext,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    LOG_DENSITY_IDX,
+    LOG_TEMPERATURE_IDX,
+    VELOCITY_SLICE,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.optimizer import (
+    OptimizeSolarWindParamsResult,
+)
+
+
+def compute_hc3_parameter_covariance(
+    jacobian: ndarray, residuals: ndarray
+) -> ndarray:
+    n_params = jacobian.shape[1]
+    try:
+        JT_J_pseudoinverse = np.linalg.pinv(jacobian.T @ jacobian)
+        h_ii = np.einsum(
+            "ki,ij,jk->k",
+            jacobian,
+            JT_J_pseudoinverse,
+            jacobian.T,
+        )
+        h_ii_clipped = np.clip(h_ii, 0.0, 0.9999)
+        hc3_weights = (residuals / (1.0 - h_ii_clipped)) ** 2
+        sandwich_middle = np.einsum(
+            "ki,k,kj->ij", jacobian, hc3_weights, jacobian
+        )
+        return JT_J_pseudoinverse @ sandwich_middle @ JT_J_pseudoinverse
+    except np.linalg.LinAlgError:
+        return np.full((n_params, n_params), np.nan)
+
+
+def r_squared(residuals: ndarray, data: ndarray) -> float:
+    data_mean = float(np.mean(data))
+    ss_tot = float(np.sum((data - data_mean) ** 2))
+    if ss_tot <= 0.0:
+        return float("nan")
+    ss_res = float(np.sum(residuals**2))
+    return 1.0 - ss_res / ss_tot
+
+
+def derive_uncertainties(
+    result: OptimizeSolarWindParamsResult,
+    ctx: SolarWindFitContext,
+) -> tuple[float, float, ndarray]:
+    parameter_covariance = compute_hc3_parameter_covariance(
+        result.jacobian, result.residuals
+    )
+    if not np.all(np.isfinite(parameter_covariance)):
+        return np.nan, np.nan, np.full((3, 3), np.nan)
+
+    log_density_variance = parameter_covariance[LOG_DENSITY_IDX, LOG_DENSITY_IDX]
+    log_temperature_variance = parameter_covariance[
+        LOG_TEMPERATURE_IDX, LOG_TEMPERATURE_IDX
+    ]
+
+    density_error = float(
+        result.sw_params.density * np.sqrt(max(log_density_variance, 0.0))
+    )
+    temperature_error = float(
+        result.sw_params.temperature * np.sqrt(max(log_temperature_variance, 0.0))
+    )
+    velocity_covariance = parameter_covariance[VELOCITY_SLICE, VELOCITY_SLICE]
+
+    return (
+        density_error,
+        temperature_error,
+        velocity_covariance,
+    )
+
+
+def make_correlated_velocity(
+    nominal: ndarray, covariance: ndarray
+) -> tuple[UFloat, UFloat, UFloat]:
+    is_finite = np.all(np.isfinite(covariance))
+    is_positive_semidefinite = is_finite and np.linalg.eigvalsh(covariance)[0] >= 0
+    if not is_positive_semidefinite:
+        return tuple(ufloat(float(v), np.nan) for v in nominal)
+
+    return tuple(correlated_values(nominal, covariance))
diff --git a/imap_l3_processing/swapi/l3a/science/solar_wind/utils.py b/imap_l3_processing/swapi/l3a/science/solar_wind/utils.py
new file mode 100644
index 000000000..00f8a785b
--- /dev/null
+++ b/imap_l3_processing/swapi/l3a/science/solar_wind/utils.py
@@ -0,0 +1,32 @@
+import math
+
+import numba
+import numpy as np
+from numpy import ndarray
+
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    SolarWindParams,
+    thermal_speed,
+)
+from imap_l3_processing.swapi.response.swapi_response import ResponseGrid
+
+
+def average_spin_axis_rtn(rotation_matrices: ndarray) -> ndarray:
+    boresight_direction_in_RTN = rotation_matrices[:, :, 1]
+    mean_direction = boresight_direction_in_RTN.mean(axis=0)
+    normalized_mean_direction = mean_direction / np.linalg.norm(mean_direction)
+    return normalized_mean_direction
+
+
+@numba.njit
+def count_rate_conversion_factor(
+    sw_params: SolarWindParams, response_grid: ResponseGrid
+) -> float:
+    sigma = thermal_speed(sw_params)
+    return (
+        response_grid.central_effective_area
+        * sw_params.density
+        * (np.sqrt(2 * np.pi) * sigma) ** -3
+        * (math.pi / 180.0) ** 2
+        * 1e5  # km -> cm
+    )
diff --git a/imap_l3_processing/swapi/l3a/science/speed_calculation.py b/imap_l3_processing/swapi/l3a/science/speed_calculation.py
deleted file mode 100644
index 51d5e548d..000000000
--- a/imap_l3_processing/swapi/l3a/science/speed_calculation.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import numpy as np
-from uncertainties import wrap
-from uncertainties.unumpy import nominal_values
-
-
-def get_peak_indices(count_rates, width, mask=True) -> slice:
-    max_indices = np.argwhere(
-            (count_rates == np.max(count_rates, where=mask, initial=0)) & mask)
-    left_min_index = np.min(max_indices)
-    right_min_index = np.max(max_indices)
-
-    if right_min_index - left_min_index > 1:
-        raise Exception("Count rates contains multiple distinct peaks")
-
-    return slice(max(0, left_min_index - width), right_min_index + width+1)
-
-def find_peak_center_of_mass_index(peak_slice, count_rates, minimum_count_rate=0, minimum_bin_count=0):
-    count_rates = np.asarray(count_rates)
-    indices = np.arange(len(count_rates))
-    peak_indices = indices[peak_slice]
-    peak_counts = count_rates[peak_slice]
-    at_least_minimum = nominal_values(peak_counts) >= minimum_count_rate
-
-    filtered_peak_indices = peak_indices[at_least_minimum]
-
-    if len(filtered_peak_indices) < minimum_bin_count:
-        raise Exception("Too few bins after removing low count rates")
-
-    filtered_peak_counts = peak_counts[at_least_minimum]
-    center_of_mass_index = np.sum(filtered_peak_indices * filtered_peak_counts) / np.sum(filtered_peak_counts)
-    return center_of_mass_index
-
-def interpolate_energy(center_of_mass_index, energies):
-    interpolate_lambda = lambda x: np.exp(np.interp(x, np.arange(len(energies)), np.log(energies)))
-    interpolate = wrap(interpolate_lambda)
-    return interpolate(center_of_mass_index)
-
-
-def times_for_sweep(start_time):
-    time_step_per_bin = 12 / 72
-    return start_time + time_step_per_bin * np.arange(72)
-
-
-def extract_coarse_sweep(data: np.ndarray):
-    if data.ndim > 1:
-        return data[:, 1:63]
-    else:
-        return data[1:63]
\ No newline at end of file
diff --git a/imap_l3_processing/swapi/l3a/swapi_l3a_dependencies.py b/imap_l3_processing/swapi/l3a/swapi_l3a_dependencies.py
index 02d3d37b8..a19b3b990 100644
--- a/imap_l3_processing/swapi/l3a/swapi_l3a_dependencies.py
+++ b/imap_l3_processing/swapi/l3a/swapi_l3a_dependencies.py
@@ -1,29 +1,27 @@
 from dataclasses import dataclass
 from pathlib import Path
+from typing import Optional
 
 from imap_data_access import download
 from imap_data_access.processing_input import ProcessingInputCollection
 from spacepy.pycdf import CDF
 
+from imap_l3_processing.models import MagData
 from imap_l3_processing.swapi.descriptors import SWAPI_L2_DESCRIPTOR, \
-    PROTON_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR, \
-    ALPHA_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR, CLOCK_ANGLE_AND_FLOW_DEFLECTION_LOOKUP_TABLE_DESCRIPTOR, \
     GEOMETRIC_FACTOR_PUI_LOOKUP_TABLE_DESCRIPTOR, INSTRUMENT_RESPONSE_LOOKUP_TABLE_DESCRIPTOR, \
     DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR, EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR, HYDROGEN_INFLOW_VECTOR_DESCRIPTOR, \
-    HELIUM_INFLOW_VECTOR_DESCRIPTOR
+    HELIUM_INFLOW_VECTOR_DESCRIPTOR, AZIMUTHAL_TRANSMISSION_DESCRIPTOR, \
+    CENTRAL_EFFECTIVE_AREA_DESCRIPTOR, PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR, \
+    MAG_RTN_DESCRIPTOR
 from imap_l3_processing.swapi.l3a.models import SwapiL2Data
-from imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_temperature_and_density import \
-    AlphaTemperatureDensityCalibrationTable
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_clock_and_deflection_angles import \
-    ClockAngleCalibrationTable
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_temperature_and_density import \
-    ProtonTemperatureAndDensityCalibrationTable
 from imap_l3_processing.swapi.l3a.science.density_of_neutral_helium_lookup_table import \
     DensityOfNeutralHeliumLookupTable
 from imap_l3_processing.swapi.l3a.science.inflow_vector import InflowVector
-from imap_l3_processing.swapi.l3a.utils import read_l2_swapi_data
+from imap_l3_processing.swapi.l3a.utils import read_l2_swapi_data, read_mag_rtn_data
+from imap_l3_processing.utils import select_mag_path
 from imap_l3_processing.swapi.l3b.science.efficiency_calibration_table import EfficiencyCalibrationTable
 from imap_l3_processing.swapi.l3b.science.geometric_factor_calibration_table import GeometricFactorCalibrationTable
+from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
 from imap_l3_processing.swapi.l3b.science.instrument_response_lookup_table import \
     InstrumentResponseLookupTableCollection
 
@@ -31,85 +29,70 @@
 @dataclass
 class SwapiL3ADependencies:
     data: SwapiL2Data
-    proton_temperature_density_calibration_table: ProtonTemperatureAndDensityCalibrationTable
-    alpha_temperature_density_calibration_table: AlphaTemperatureDensityCalibrationTable
-    clock_angle_and_flow_deflection_calibration_table: ClockAngleCalibrationTable
     efficiency_calibration_table: EfficiencyCalibrationTable
     geometric_factor_calibration_table: GeometricFactorCalibrationTable
     instrument_response_calibration_table: InstrumentResponseLookupTableCollection
     density_of_neutral_helium_calibration_table: DensityOfNeutralHeliumLookupTable
     hydrogen_inflow_vector: InflowVector
     helium_inflow_vector: InflowVector
+    swapi_response: SwapiResponse
+    mag_data: Optional[MagData] = None
+    mag_is_preliminary: bool = False
 
     @classmethod
     def fetch_dependencies(cls, dependencies: ProcessingInputCollection):
         # @formatter:off
         science_dependency_file = dependencies.get_file_paths(source='swapi', descriptor=SWAPI_L2_DESCRIPTOR)
-        proton_density_and_temperature_calibration_file = dependencies.get_file_paths(source='swapi', descriptor=PROTON_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR)
-        alpha_density_and_temperature_calibration_file = dependencies.get_file_paths(source='swapi', descriptor=ALPHA_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR)
-        clock_and_deflection_file = dependencies.get_file_paths(source='swapi', descriptor=CLOCK_ANGLE_AND_FLOW_DEFLECTION_LOOKUP_TABLE_DESCRIPTOR)
         efficiency_calibration_table = dependencies.get_file_paths(source='swapi', descriptor=EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR)
         geometric_factor_calibration_table = dependencies.get_file_paths(source='swapi', descriptor=GEOMETRIC_FACTOR_PUI_LOOKUP_TABLE_DESCRIPTOR)
         instrument_response_table = dependencies.get_file_paths(source='swapi', descriptor=INSTRUMENT_RESPONSE_LOOKUP_TABLE_DESCRIPTOR)
         neutral_helium_table = dependencies.get_file_paths(source='swapi', descriptor=DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR)
         hydrogen_vector_paths = dependencies.get_file_paths(source='swapi', descriptor=HYDROGEN_INFLOW_VECTOR_DESCRIPTOR)
         helium_vector_paths = dependencies.get_file_paths(source='swapi', descriptor=HELIUM_INFLOW_VECTOR_DESCRIPTOR)
+        azimuthal_transmission_paths = dependencies.get_file_paths(source='swapi', descriptor=AZIMUTHAL_TRANSMISSION_DESCRIPTOR)
+        central_effective_area_paths = dependencies.get_file_paths(source='swapi', descriptor=CENTRAL_EFFECTIVE_AREA_DESCRIPTOR)
+        passband_fit_coefficients_paths = dependencies.get_file_paths(source='swapi', descriptor=PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR)
         # @formatter:on
 
-        science_download_path = download(science_dependency_file[0])
-        proton_density_and_temperature_calibration_file_path = download(
-            proton_density_and_temperature_calibration_file[0])
-        alpha_density_and_temperature_calibration_file_path = download(
-            alpha_density_and_temperature_calibration_file[0])
-        clock_and_deflection_file_path = download(clock_and_deflection_file[0])
-        efficiency_calibration_table_path = download(efficiency_calibration_table[0])
-        geometric_factor_calibration_table_path = download(geometric_factor_calibration_table[0])
-        instrument_response_table_path = download(instrument_response_table[0])
-        neutral_helium_table_path = download(neutral_helium_table[0])
-        hydrogen_vector_path = download(hydrogen_vector_paths[0])
-        helium_vector_path = download(helium_vector_paths[0])
+        mag_path, mag_level = select_mag_path(dependencies, MAG_RTN_DESCRIPTOR)
+        mag_is_preliminary = mag_level == "l1d"
 
         return cls.from_file_paths(
-            science_download_path,
-            proton_density_and_temperature_calibration_file_path,
-            alpha_density_and_temperature_calibration_file_path,
-            clock_and_deflection_file_path,
-            efficiency_calibration_table_path,
-            geometric_factor_calibration_table_path,
-            instrument_response_table_path,
-            neutral_helium_table_path,
-            hydrogen_vector_path,
-            helium_vector_path
+            download(science_dependency_file[0]),
+            download(efficiency_calibration_table[0]),
+            download(geometric_factor_calibration_table[0]),
+            download(instrument_response_table[0]),
+            download(neutral_helium_table[0]),
+            download(hydrogen_vector_paths[0]),
+            download(helium_vector_paths[0]),
+            download(azimuthal_transmission_paths[0]),
+            download(central_effective_area_paths[0]),
+            download(passband_fit_coefficients_paths[0]),
+            mag_path,
+            mag_is_preliminary,
         )
 
     @classmethod
-    def from_file_paths(cls, science_dependency_path: Path, proton_density_and_temperature_calibration_path: Path,
-                        alpha_density_and_temperature_calibration_path: Path, clock_and_deflection_file_path: Path,
+    def from_file_paths(cls, science_dependency_path: Path,
                         efficiency_calibration_path: Path, geometric_factor_calibration_path: Path,
                         instrument_response_path: Path, neutral_helium_path: Path, hydrogen_inflow_vector_path: Path,
-                        helium_inflow_vector_path: Path):
-        swapi_l2_data = read_l2_swapi_data(CDF(str(science_dependency_path)))
-        proton_density_temp_lookup = ProtonTemperatureAndDensityCalibrationTable.from_file(
-            proton_density_and_temperature_calibration_path)
-        alpha_density_temp_lookup = AlphaTemperatureDensityCalibrationTable.from_file(
-            alpha_density_and_temperature_calibration_path)
-        clock_deflection_lookup = ClockAngleCalibrationTable.from_file(clock_and_deflection_file_path)
-        efficiency_lookup = EfficiencyCalibrationTable(efficiency_calibration_path)
-        geometric_factor_calibration_lookup = GeometricFactorCalibrationTable.from_file(
-            geometric_factor_calibration_path)
-        instrument_response_lookup = InstrumentResponseLookupTableCollection.from_file(instrument_response_path)
-        neutral_helium_lookup = DensityOfNeutralHeliumLookupTable.from_file(neutral_helium_path)
-
-        hydrogen_inflow_vector = InflowVector.from_file(hydrogen_inflow_vector_path)
-        helium_inflow_vector = InflowVector.from_file(helium_inflow_vector_path)
-
-        return cls(swapi_l2_data,
-                   proton_density_temp_lookup,
-                   alpha_density_temp_lookup,
-                   clock_deflection_lookup,
-                   efficiency_lookup,
-                   geometric_factor_calibration_lookup,
-                   instrument_response_lookup,
-                   neutral_helium_lookup,
-                   hydrogen_inflow_vector,
-                   helium_inflow_vector)
+                        helium_inflow_vector_path: Path, azimuthal_transmission_path: Path,
+                        central_effective_area_path: Path, passband_fit_coefficients_path: Path,
+                        mag_path: Optional[Path] = None,
+                        mag_is_preliminary: bool = False):
+        return cls(
+            data=read_l2_swapi_data(CDF(str(science_dependency_path))),
+            efficiency_calibration_table=EfficiencyCalibrationTable(efficiency_calibration_path),
+            geometric_factor_calibration_table=GeometricFactorCalibrationTable.from_file(
+                geometric_factor_calibration_path),
+            instrument_response_calibration_table=InstrumentResponseLookupTableCollection.from_file(
+                instrument_response_path),
+            density_of_neutral_helium_calibration_table=DensityOfNeutralHeliumLookupTable.from_file(
+                neutral_helium_path),
+            hydrogen_inflow_vector=InflowVector.from_file(hydrogen_inflow_vector_path),
+            helium_inflow_vector=InflowVector.from_file(helium_inflow_vector_path),
+            swapi_response=SwapiResponse.from_files(
+                azimuthal_transmission_path, central_effective_area_path, passband_fit_coefficients_path),
+            mag_data=read_mag_rtn_data(mag_path) if mag_path is not None else None,
+            mag_is_preliminary=mag_is_preliminary,
+        )
diff --git a/imap_l3_processing/swapi/l3a/utils.py b/imap_l3_processing/swapi/l3a/utils.py
index 048764e60..bf7a92f1d 100644
--- a/imap_l3_processing/swapi/l3a/utils.py
+++ b/imap_l3_processing/swapi/l3a/utils.py
@@ -1,28 +1,135 @@
 from datetime import datetime
 from typing import Iterable
 
+import numpy as np
+import scipy.optimize
+from numpy import ndarray
 from spacepy import pycdf
 from spacepy.pycdf import CDF
+from uncertainties import UFloat, umath, unumpy
 
 from imap_l3_processing.cdf.cdf_utils import read_numeric_variable
+from imap_l3_processing.constants import (
+    METERS_PER_KILOMETER,
+    ONE_SECOND_IN_NANOSECONDS,
+    THIRTY_SECONDS_IN_NANOSECONDS,
+)
+from imap_l3_processing.models import MagData
 from imap_l3_processing.swapi.l3a.models import SwapiL2Data
+from imap_processing.spice.geometry import (
+    SpiceFrame,
+    frame_transform,
+    get_rotation_matrix,
+    imap_state,
+)
+from imap_processing.spice.time import ttj2000ns_to_et
+
+from imap_l3_processing.swapi.response.deadtime import deadtime_factor
+
+
+def calculate_sw_speed(particle_mass, particle_charge, energy):
+    speed_squared = 2 * energy * particle_charge / particle_mass
+    if isinstance(energy, UFloat):
+        return umath.sqrt(speed_squared) / METERS_PER_KILOMETER
+    if isinstance(energy, np.ndarray) and energy.dtype == object:
+        return unumpy.sqrt(speed_squared) / METERS_PER_KILOMETER
+    return np.sqrt(speed_squared) / METERS_PER_KILOMETER
+
+
+def read_mag_rtn_data(cdf_path) -> MagData:
+    with CDF(str(cdf_path)) as cdf:
+        var = cdf["b_rtn"]
+        data = read_numeric_variable(var)[:, :3]
+        attrs = var.attrs
+        if "VALIDMIN" in attrs:
+            data = np.where(data < float(attrs["VALIDMIN"]), np.nan, data)
+        if "VALIDMAX" in attrs:
+            data = np.where(data > float(attrs["VALIDMAX"]), np.nan, data)
+        return MagData(
+            epoch=pycdf.lib.v_datetime_to_tt2000(cdf["epoch"][...]),
+            mag_data=data,
+        )
 
 
 def read_l2_swapi_data(cdf: CDF) -> SwapiL2Data:
     sci_start_times = pycdf.lib.v_datetime_to_tt2000(
-        [datetime.fromisoformat(x) for x in cdf["sci_start_time"][...]])
-    return SwapiL2Data(sci_start_times,
-                       read_numeric_variable(cdf["esa_energy"]),
-                       read_numeric_variable(cdf["swp_coin_rate"]),
-                       read_numeric_variable(cdf["swp_coin_rate_stat_uncert_plus"]))
+        [datetime.fromisoformat(x) for x in cdf["sci_start_time"][...]]
+    )
+    return SwapiL2Data(
+        sci_start_times,
+        read_numeric_variable(cdf["esa_energy"]),
+        read_numeric_variable(cdf["swp_coin_rate"]),
+        read_numeric_variable(cdf["swp_coin_rate_stat_uncert_plus"]),
+    )
+
+
+def get_swapi_geometry(measurement_time: ndarray) -> ndarray:
+    et_times = ttj2000ns_to_et(np.atleast_1d(measurement_time))
+    return get_rotation_matrix(et_times, SpiceFrame.IMAP_SWAPI, SpiceFrame.IMAP_RTN)
+
+
+def rotate_rtn_to_dps(vector_rtn, epoch_tt2000_ns: float):
+    et = float(ttj2000ns_to_et(epoch_tt2000_ns))
+    return frame_transform(
+        et, np.asarray(vector_rtn), SpiceFrame.IMAP_RTN, SpiceFrame.IMAP_DPS
+    )
+
+
+def get_spacecraft_velocity_rtn(epoch_tt2000_ns: float) -> ndarray:
+    et = float(ttj2000ns_to_et(epoch_tt2000_ns))
+    state_eclipj2000 = imap_state(et, SpiceFrame.ECLIPJ2000)
+    rtn_from_eclipj2000 = get_rotation_matrix(
+        et, SpiceFrame.ECLIPJ2000, SpiceFrame.IMAP_RTN
+    )
+    return np.einsum("ij,j->i", rtn_from_eclipj2000, state_eclipj2000[3:])
+
+
+def compute_direction_of_mean_magnetic_field_over_chunk(
+    mag_data,
+    chunk_epoch_center_tt2000_ns: int,
+    chunk_epoch_delta_ns: int,
+) -> np.ndarray:
+    start = chunk_epoch_center_tt2000_ns - chunk_epoch_delta_ns
+    end = chunk_epoch_center_tt2000_ns + chunk_epoch_delta_ns
+    left = np.searchsorted(mag_data.epoch, start, side="left")
+    right = np.searchsorted(mag_data.epoch, end, side="left")
+    if right == left:
+        return np.full(3, np.nan)
+    b_mean = mag_data.mag_data[left:right].mean(axis=0)
+    if not np.all(np.isfinite(b_mean)):
+        return np.full(3, np.nan)
+    return b_mean / np.linalg.norm(b_mean)
+
 
 def chunk_l2_data(data: SwapiL2Data, chunk_size: int) -> Iterable[SwapiL2Data]:
-    i = 0
-    while i < len(data.sci_start_time):
+    n = len(data.sci_start_time)
+    for i in range(0, n - n % chunk_size, chunk_size):
         yield SwapiL2Data(
-            data.sci_start_time[i:i + chunk_size],
-            data.energy[i:i + chunk_size],
-            data.coincidence_count_rate[i:i + chunk_size],
-            data.coincidence_count_rate_uncertainty[i:i + chunk_size]
+            data.sci_start_time[i : i + chunk_size],
+            data.energy[i : i + chunk_size],
+            data.coincidence_count_rate[i : i + chunk_size],
+            data.coincidence_count_rate_uncertainty[i : i + chunk_size],
         )
-        i += chunk_size
+
+
+def chunk_epoch(chunk: SwapiL2Data) -> float:
+    return chunk.sci_start_time[0] + THIRTY_SECONDS_IN_NANOSECONDS
+
+
+def measurement_times(chunk: SwapiL2Data, bin_slice: slice) -> ndarray:
+    bins = np.arange(bin_slice.start, bin_slice.stop)
+    return (
+        chunk.sci_start_time[:, np.newaxis]
+        + bins * (12 / 72 * ONE_SECOND_IN_NANOSECONDS)
+    ).flatten()
+
+
+def optimal_density_scale(unit_ideal_rates: ndarray, observed_rates: ndarray) -> float:
+    def predicted_observed_rate(unit_rate, density):
+        true_rate = density * unit_rate
+        return true_rate * deadtime_factor(true_rate)
+
+    popt, _ = scipy.optimize.curve_fit(
+        f=predicted_observed_rate, xdata=unit_ideal_rates, ydata=observed_rates, p0=[1]
+    )
+    return float(popt[0])
diff --git a/imap_l3_processing/swapi/l3b/science/calculate_solar_wind_vdf.py b/imap_l3_processing/swapi/l3b/science/calculate_solar_wind_vdf.py
index 58483603b..96d8e6fcb 100644
--- a/imap_l3_processing/swapi/l3b/science/calculate_solar_wind_vdf.py
+++ b/imap_l3_processing/swapi/l3b/science/calculate_solar_wind_vdf.py
@@ -8,7 +8,7 @@
 from imap_l3_processing.constants import PROTON_MASS_KG, PROTON_CHARGE_COULOMBS, ALPHA_PARTICLE_CHARGE_COULOMBS, \
     ALPHA_PARTICLE_MASS_KG, HE_PUI_PARTICLE_MASS_KG, PUI_PARTICLE_CHARGE_COULOMBS, METERS_PER_KILOMETER, \
     CENTIMETERS_PER_METER
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_speed import calculate_sw_speed
+from imap_l3_processing.swapi.l3a.utils import calculate_sw_speed
 from imap_l3_processing.swapi.l3b.science.geometric_factor_calibration_table import GeometricFactorCalibrationTable
 
 
diff --git a/imap_l3_processing/swapi/l3b/science/efficiency_calibration_table.py b/imap_l3_processing/swapi/l3b/science/efficiency_calibration_table.py
index 4935dc9a1..43175c2d4 100644
--- a/imap_l3_processing/swapi/l3b/science/efficiency_calibration_table.py
+++ b/imap_l3_processing/swapi/l3b/science/efficiency_calibration_table.py
@@ -12,6 +12,14 @@ def get_proton_efficiency_for(self, time_as_tt2000) -> float:
     def get_alpha_efficiency_for(self, time_as_tt2000) -> float:
         return self._get_efficiency_for_index("alpha efficiency", time_as_tt2000)
 
+    @property
+    def eps_p_lab(self) -> float:
+        cutoff = np.datetime64("2025-11-01", "ns")
+        for d in self.data:
+            if d["time"] >= cutoff:
+                return float(d["proton efficiency"])
+        return float(self.data[0]["proton efficiency"])
+
     def _get_efficiency_for_index(self, name, time_as_tt2000) -> float:
         for d in reversed(self.data):
             if d["time"] < np.datetime64(pycdf.lib.tt2000_to_datetime(int(time_as_tt2000)), "ns"):
diff --git a/imap_l3_processing/swapi/l3b/science/instrument_response_lookup_table.py b/imap_l3_processing/swapi/l3b/science/instrument_response_lookup_table.py
index 885202d87..5925bfec4 100644
--- a/imap_l3_processing/swapi/l3b/science/instrument_response_lookup_table.py
+++ b/imap_l3_processing/swapi/l3b/science/instrument_response_lookup_table.py
@@ -6,7 +6,7 @@
 import numpy as np
 
 from imap_l3_processing.constants import HE_PUI_PARTICLE_MASS_KG, PUI_PARTICLE_CHARGE_COULOMBS
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_speed import calculate_sw_speed
+from imap_l3_processing.swapi.l3a.utils import calculate_sw_speed
 
 
 @dataclass
diff --git a/imap_l3_processing/swapi/quality_flags.py b/imap_l3_processing/swapi/quality_flags.py
index b0e3de371..7b9c60ad1 100644
--- a/imap_l3_processing/swapi/quality_flags.py
+++ b/imap_l3_processing/swapi/quality_flags.py
@@ -3,6 +3,7 @@
 
 class SwapiL3Flags(FlagNameMixin):
     NONE = CommonFlags.NONE
-    SWP_SW_ANGLES_ESTIMATED = 2 ** 2
-    HI_CHI_SQ = 2 ** 3
-    PUI_FIT_MISSING_UNCERTAINTY = 2 ** 4
+    BAD_FIT = 2**2
+    FIT_ERROR = 2**3
+    PRELIMINARY_MAG = 2**4
+    PUI_FIT_MISSING_UNCERTAINTY = 2**15  # will be removed in PUI update
diff --git a/imap_l3_processing/swapi/response/__init__.py b/imap_l3_processing/swapi/response/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/imap_l3_processing/swapi/response/azimuthal_transmission.py b/imap_l3_processing/swapi/response/azimuthal_transmission.py
new file mode 100644
index 000000000..ac747e516
--- /dev/null
+++ b/imap_l3_processing/swapi/response/azimuthal_transmission.py
@@ -0,0 +1,34 @@
+import math
+from typing import NamedTuple
+
+import numba
+from numpy import ndarray
+
+
+class AzimuthalTransmissionGrid(NamedTuple):
+    values: ndarray
+    spacing: float
+
+
+@numba.njit
+def interpolate_azimuthal_transmission(
+    grid: AzimuthalTransmissionGrid,
+    azimuth: float,
+) -> float:
+    azimuth = (azimuth + 180) % 360 - 180
+    i_float = abs(azimuth) / grid.spacing
+    i_lower = int(math.floor(i_float))
+    i_upper = i_lower + 1
+
+    n = len(grid.values)
+    if i_lower >= n:
+        i_lower = n - 1
+    if i_upper >= n:
+        i_upper = n - 1
+
+    weight_lower = float(i_upper) - i_float
+    weight_upper = i_float - float(i_lower)
+    return (
+        grid.values[i_lower] * weight_lower
+        + grid.values[i_upper] * weight_upper
+    )
diff --git a/imap_l3_processing/swapi/response/deadtime.py b/imap_l3_processing/swapi/response/deadtime.py
new file mode 100644
index 000000000..7120a3db6
--- /dev/null
+++ b/imap_l3_processing/swapi/response/deadtime.py
@@ -0,0 +1,8 @@
+import numba
+
+SWAPI_DEADTIME_S = 183.7e-9
+
+
+@numba.njit
+def deadtime_factor(true_rate):
+    return 1.0 / (1.0 + SWAPI_DEADTIME_S * true_rate)
diff --git a/imap_l3_processing/swapi/response/passband_grid.py b/imap_l3_processing/swapi/response/passband_grid.py
new file mode 100644
index 000000000..96a54cc5e
--- /dev/null
+++ b/imap_l3_processing/swapi/response/passband_grid.py
@@ -0,0 +1,203 @@
+from typing import NamedTuple
+
+import numba
+import numpy as np
+import pandas as pd
+from numpy.typing import NDArray
+
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+
+_TARGET_ELEVATIONS = np.arange(-15, 15 + 0.5, 0.5)
+_TARGET_SPEED_RATIOS = np.linspace(0.9, 1.1, 101)
+_PASSBAND_BOUNDARY_THRESHOLD = 1e-2
+
+
+class PassbandGrid(NamedTuple):
+    min_elevation: float
+    elevation_spacing: float
+    min_speed_ratio: float
+    speed_ratio_spacing: float
+    values: NDArray
+    min_boundary: NDArray
+    max_boundary: NDArray
+    elevation_range: tuple
+
+
+def build_passband_grid(values_df: pd.DataFrame) -> PassbandGrid:
+    grid_values = _build_passband_array(
+        values_df, _TARGET_ELEVATIONS, _TARGET_SPEED_RATIOS
+    )
+    min_boundary, max_boundary = _passband_boundaries(grid_values, _TARGET_SPEED_RATIOS)
+    elevation_range = _elevation_range(grid_values, _TARGET_ELEVATIONS)
+
+    return PassbandGrid(
+        min_elevation=float(_TARGET_ELEVATIONS[0]),
+        elevation_spacing=float(_TARGET_ELEVATIONS[1] - _TARGET_ELEVATIONS[0]),
+        min_speed_ratio=float(_TARGET_SPEED_RATIOS[0]),
+        speed_ratio_spacing=float(_TARGET_SPEED_RATIOS[1] - _TARGET_SPEED_RATIOS[0]),
+        values=grid_values,
+        min_boundary=min_boundary,
+        max_boundary=max_boundary,
+        elevation_range=elevation_range,
+    )
+
+
+# without the explicit override `fastmath=False`, there was an issue with isnan()
+@numba.njit(fastmath=False)
+def speed_ratio_range_at_elevation(grid, elevation: float):
+    n_elevations = grid.min_boundary.shape[0]
+    i_float = (elevation - grid.min_elevation) / grid.elevation_spacing
+    if i_float < 0.0:
+        lower_row = 0
+    elif i_float >= n_elevations - 1:
+        lower_row = n_elevations - 1
+    else:
+        lower_row = int(i_float)
+    upper_row = lower_row + 1 if lower_row + 1 < n_elevations else n_elevations - 1
+
+    lower_min = grid.min_boundary[lower_row]
+    upper_min = grid.min_boundary[upper_row]
+    lower_max = grid.max_boundary[lower_row]
+    upper_max = grid.max_boundary[upper_row]
+
+    lower_is_nan = np.isnan(lower_min) or np.isnan(lower_max)
+    upper_is_nan = np.isnan(upper_min) or np.isnan(upper_max)
+    if lower_is_nan and upper_is_nan:
+        raise ValueError("Elevation out of range.")
+    if lower_is_nan:
+        return upper_min, upper_max
+    if upper_is_nan:
+        return lower_min, lower_max
+    lo = lower_min if lower_min < upper_min else upper_min
+    hi = lower_max if lower_max > upper_max else upper_max
+    return lo, hi
+
+
+@numba.njit
+def interpolate_passband(grid, elevation: float, speed_ratio: float) -> float:
+    return _bilinear_interpolate(
+        values=grid.values,
+        x0=grid.min_speed_ratio,
+        dx=grid.speed_ratio_spacing,
+        y0=grid.min_elevation,
+        dy=grid.elevation_spacing,
+        x=speed_ratio,
+        y=elevation,
+    )
+
+
+@numba.njit
+def _bilinear_interpolate(
+    values: NDArray,
+    x0: float,
+    dx: float,
+    y0: float,
+    dy: float,
+    x: float,
+    y: float,
+) -> float:
+    i_float = (y - y0) / dy
+    if i_float < 0 or i_float + 1 >= values.shape[0]:
+        return 0.0
+    j_float = (x - x0) / dx
+    if j_float < 0 or j_float + 1 >= values.shape[1]:
+        return 0.0
+
+    i_lower = int(i_float)
+    i_upper = i_lower + 1
+    i_weight = i_float - i_lower
+    j_lower = int(j_float)
+    j_upper = j_lower + 1
+    j_weight = j_float - j_lower
+
+    return (1 - i_weight) * (
+        (1 - j_weight) * values[i_lower, j_lower] + j_weight * values[i_lower, j_upper]
+    ) + i_weight * (
+        (1 - j_weight) * values[i_upper, j_lower] + j_weight * values[i_upper, j_upper]
+    )
+
+
+def _build_passband_array(
+    values_df: pd.DataFrame, target_elevations: NDArray, target_speed_ratios: NDArray
+) -> NDArray:
+    pivot = values_df.reset_index()
+    pivot["speed_ratio"] = np.sqrt(pivot["energy_ratio"] / SWAPI_K_FACTOR)
+    pivot = (
+        pivot.drop(columns="energy_ratio")
+        .set_index(["elevation", "speed_ratio"])["value"]
+        .unstack("speed_ratio")
+    )
+    pivot = pivot.fillna(0.0)
+
+    src_speed_ratios = pivot.columns.values
+    result = np.zeros((len(target_elevations), len(target_speed_ratios)))
+    for i, elev in enumerate(target_elevations):
+        if elev in pivot.index:
+            result[i] = np.interp(
+                target_speed_ratios,
+                src_speed_ratios,
+                pivot.loc[elev].values,
+                left=0.0,
+                right=0.0,
+            )
+
+    # Normalize so the central value (elevation=0, speed_ratio=1) is 1.
+    central_value = _bilinear_interpolate(
+        values=result,
+        x0=target_speed_ratios[0],
+        dx=target_speed_ratios[1] - target_speed_ratios[0],
+        y0=target_elevations[0],
+        dy=target_elevations[1] - target_elevations[0],
+        x=1.0,
+        y=0.0,
+    )
+    result = result / central_value
+    return result.copy(order="C")
+
+
+def _passband_boundaries(
+    grid_values: NDArray, target_speed_ratios: NDArray
+) -> tuple[NDArray, NDArray]:
+    cutoff = _cutoff_value(grid_values)
+    n_elevations = grid_values.shape[0]
+    mins = np.full(n_elevations, np.nan)
+    maxs = np.full(n_elevations, np.nan)
+    for i in range(n_elevations):
+        above = np.flatnonzero(grid_values[i] >= cutoff)
+        if above.size == 0:
+            continue
+        mins[i] = _interp_cutoff_crossing(
+            target_speed_ratios, grid_values[i], cutoff, above[0], above[0] - 1
+        )
+        maxs[i] = _interp_cutoff_crossing(
+            target_speed_ratios, grid_values[i], cutoff, above[-1], above[-1] + 1
+        )
+    return mins, maxs
+
+
+def _elevation_range(grid_values: NDArray, target_elevations: NDArray) -> tuple:
+    cutoff = _cutoff_value(grid_values)
+    row_max = grid_values.max(axis=1)
+    above = np.flatnonzero(row_max >= cutoff)
+
+    lo = _interp_cutoff_crossing(
+        target_elevations, row_max, cutoff, above[0], above[0] - 1
+    )
+    hi = _interp_cutoff_crossing(
+        target_elevations, row_max, cutoff, above[-1], above[-1] + 1
+    )
+    return (float(lo), float(hi))
+
+
+def _interp_cutoff_crossing(
+    x: NDArray, y: NDArray, cutoff: float, i_inside: int, i_outside: int
+) -> float:
+    """Linear interpolation of `x` where `y` crosses `cutoff`, given that
+    `y[i_inside] >= cutoff` and `y[i_outside] < cutoff`."""
+    return x[i_inside] + (cutoff - y[i_inside]) / (y[i_outside] - y[i_inside]) * (
+        x[i_outside] - x[i_inside]
+    )
+
+
+def _cutoff_value(grid_values):
+    return _PASSBAND_BOUNDARY_THRESHOLD * float(grid_values.max())
diff --git a/imap_l3_processing/swapi/response/swapi_response.py b/imap_l3_processing/swapi/response/swapi_response.py
new file mode 100644
index 000000000..aebb2c9a8
--- /dev/null
+++ b/imap_l3_processing/swapi/response/swapi_response.py
@@ -0,0 +1,178 @@
+from pathlib import Path
+from typing import Callable, NamedTuple
+
+import numpy as np
+import pandas as pd
+from numpy.typing import NDArray
+from scipy.interpolate import interp1d
+
+from imap_l3_processing.constants import (
+    METERS_PER_KILOMETER,
+    PROTON_CHARGE_OVER_MASS_C_PER_KG,
+)
+from imap_l3_processing.swapi.response.azimuthal_transmission import (
+    AzimuthalTransmissionGrid,
+)
+from imap_l3_processing.swapi.response.passband_grid import (
+    PassbandGrid,
+    build_passband_grid,
+)
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+
+
+class ResponseGrid(NamedTuple):
+    sg_passband: PassbandGrid
+    oa_passband: PassbandGrid
+    central_speed: float
+    central_effective_area: float
+    azimuthal_transmission: AzimuthalTransmissionGrid
+
+
+class SwapiResponse:
+    AZIMUTHAL_TRANSMISSION_SPACING_DEG = 0.1
+
+    def __init__(
+        self,
+        azimuthal_transmission: NDArray,
+        central_effective_area_at_voltage: Callable,
+        passband_fit_coefficients: pd.DataFrame,
+        passband_esa_voltage_limits: dict,
+    ):
+        self._azimuthal_transmission = azimuthal_transmission
+        self._central_effective_area_at_voltage = central_effective_area_at_voltage
+        self._passband_fit_coefficients = passband_fit_coefficients
+        self._passband_esa_voltage_limits = passband_esa_voltage_limits
+        self._passband_grid_cache: dict = {}
+        self._response_grid_cache: dict = {}
+
+    @classmethod
+    def from_files(
+        cls,
+        azimuthal_transmission_path: Path,
+        central_effective_area_path: Path,
+        passband_fit_coefficients_path: Path,
+    ) -> "SwapiResponse":
+        transmission_df = pd.read_csv(azimuthal_transmission_path)
+        area_df = pd.read_csv(central_effective_area_path)
+        coeffs_df = pd.read_csv(
+            passband_fit_coefficients_path,
+            index_col=["region", "energy_ratio", "elevation"],
+        )
+        limit_cols = ["min_esa_voltage", "max_esa_voltage"]
+        limits_df = coeffs_df[limit_cols]
+        coeffs_df = coeffs_df.drop(columns=limit_cols)
+        esa_limits = {
+            region: (
+                float(limits_df.xs(region, level="region")["min_esa_voltage"].iloc[0]),
+                float(limits_df.xs(region, level="region")["max_esa_voltage"].iloc[0]),
+            )
+            for region in limits_df.index.get_level_values("region").unique()
+        }
+        coeffs_df.columns = coeffs_df.columns.astype(int)
+
+        central_effective_area_values = area_df["effective_area"].values
+        return cls(
+            azimuthal_transmission=transmission_df["transmission"].fillna(0).values,
+            central_effective_area_at_voltage=interp1d(
+                area_df["esa_voltage"].values,
+                central_effective_area_values,
+                kind="linear",
+                bounds_error=False,
+                fill_value=(
+                    float(central_effective_area_values[0]),
+                    float(central_effective_area_values[-1]),
+                ),
+                assume_sorted=True,
+            ),
+            passband_fit_coefficients=coeffs_df,
+            passband_esa_voltage_limits=esa_limits,
+        )
+
+    def _get_central_effective_area(self, esa_voltage: float) -> float:
+        return float(self._central_effective_area_at_voltage(np.abs(esa_voltage)))
+
+    def _central_speed(
+        self, esa_voltage: float, mass_per_charge_m_p_per_e: float
+    ) -> float:
+        # formula: v_0 = sqrt(2 k* |V| (e/m_p) / mass_per_charge_m_p_per_e).
+
+        return float(
+            np.sqrt(
+                2.0
+                * SWAPI_K_FACTOR
+                * abs(esa_voltage)
+                * PROTON_CHARGE_OVER_MASS_C_PER_KG
+                / float(mass_per_charge_m_p_per_e)
+            )
+            / METERS_PER_KILOMETER
+        )
+
+    def warm_cache(self, esa_voltages) -> None:
+        for v in np.unique(np.asarray(esa_voltages, dtype=float).ravel()):
+            key = self._cache_key(v)
+            if np.isfinite(v) and key not in self._passband_grid_cache:
+                self._passband_grid_cache[key] = dict()
+                for region in ("SG", "OA"):
+                    passband = build_passband_grid(self._get_passband_values(v, region))
+                    self._passband_grid_cache[key][region] = passband
+
+    def get_response_grid(
+        self,
+        esa_voltage: float,
+        mass_per_charge_m_p_per_e: float,
+        central_effective_area_scale: float = 1.0,
+    ) -> ResponseGrid:
+        cache_key = (
+            self._cache_key(float(esa_voltage)),
+            self._cache_key(float(mass_per_charge_m_p_per_e)),
+            self._cache_key(float(central_effective_area_scale)),
+        )
+
+        cached = self._response_grid_cache.get(cache_key)
+        if cached is not None:
+            return cached
+
+        passband_cache_key = self._cache_key(esa_voltage)
+        if passband_cache_key not in self._passband_grid_cache:
+            raise KeyError(
+                f"No PassbandGrid cached for ESA voltage {esa_voltage} V. "
+                f"Call warm_cache([{esa_voltage}]) first."
+            )
+        passband_pair = self._passband_grid_cache[passband_cache_key]
+
+        sg_passband = passband_pair["SG"]
+        oa_passband = passband_pair["OA"]
+
+        response_grid = ResponseGrid(
+            sg_passband=sg_passband,
+            oa_passband=oa_passband,
+            central_speed=self._central_speed(esa_voltage, mass_per_charge_m_p_per_e),
+            central_effective_area=(
+                self._get_central_effective_area(esa_voltage)
+                * float(central_effective_area_scale)
+            ),
+            azimuthal_transmission=AzimuthalTransmissionGrid(
+                values=np.asarray(self._azimuthal_transmission, dtype=float),
+                spacing=float(self.AZIMUTHAL_TRANSMISSION_SPACING_DEG),
+            ),
+        )
+
+        self._response_grid_cache[cache_key] = response_grid
+        return response_grid
+
+    def _get_passband_values(self, esa_voltage: float, region: str) -> pd.DataFrame:
+        v_min, v_max = self._passband_esa_voltage_limits.get(region, (0, np.inf))
+        clamped_voltage = float(np.clip(np.abs(esa_voltage), v_min, v_max))
+        coeffs = self._passband_fit_coefficients.xs(region, level="region")
+
+        # we use energy-angle passbands for modeling the coincidence rate,
+        # but fits are to voltage-angle passbands as a function of natural log of beam energy
+        # effectively equivalent as long as the x-axis set to is E/|V| and not E or |V|
+        # but need to select the one via for this voltage via the k factor
+        log_beam_energy = np.log(SWAPI_K_FACTOR * clamped_voltage)
+        values = np.exp(np.polyval(coeffs.values.T, log_beam_energy))
+
+        return pd.DataFrame(values, index=coeffs.index, columns=["value"])
+
+    def _cache_key(self, x) -> float:
+        return round(float(x), 3)
diff --git a/imap_l3_processing/swapi/swapi_processor.py b/imap_l3_processing/swapi/swapi_processor.py
index 4f9dd5e9c..158c3eff0 100644
--- a/imap_l3_processing/swapi/swapi_processor.py
+++ b/imap_l3_processing/swapi/swapi_processor.py
@@ -1,111 +1,144 @@
 import logging
-from dataclasses import replace, astuple
+from dataclasses import replace
 
 import numpy as np
 from imap_data_access.processing_input import ProcessingInputCollection
+from spacepy import pycdf
 from uncertainties import ufloat
-from uncertainties.unumpy import uarray, nominal_values
+from uncertainties.unumpy import uarray
 
-from imap_l3_processing.constants import THIRTY_SECONDS_IN_NANOSECONDS, FIVE_MINUTES_IN_NANOSECONDS
+from imap_l3_processing.constants import FIVE_MINUTES_IN_NANOSECONDS
 from imap_l3_processing.models import InputMetadata
 from imap_l3_processing.processor import Processor
-from imap_l3_processing.swapi.l3a.models import SwapiL3ProtonSolarWindData, SwapiL3AlphaSolarWindData, \
-    SwapiL3PickupIonData
-from imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_speed import calculate_alpha_solar_wind_speed, \
-    calculate_combined_sweeps
-from imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_temperature_and_density import \
-    calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps
-from imap_l3_processing.swapi.l3a.science.calculate_pickup_ion import calculate_ten_minute_velocities, \
-    calculate_pickup_ion_values, calculate_helium_pui_temperature, calculate_helium_pui_density
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_clock_and_deflection_angles import \
-    calculate_deflection_angle, calculate_clock_angle
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_speed import calculate_proton_solar_wind_speed, \
-    estimate_deflection_and_clock_angles
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_temperature_and_density import \
-    calculate_proton_solar_wind_temperature_and_density
-from imap_l3_processing.swapi.l3a.science.speed_calculation import extract_coarse_sweep
+from imap_l3_processing.swapi.l3a.chunk_fits import (
+    AlphaChunkFitter,
+    ParallelChunkRunner,
+    ProtonChunkFitter,
+)
+from imap_l3_processing.swapi.l3a.models import (
+    SwapiL3ProtonSolarWindData,
+    SwapiL3AlphaSolarWindData,
+    SwapiL3PickupIonData,
+)
+from imap_l3_processing.swapi.l3a.science.calculate_pickup_ion import (
+    calculate_ten_minute_velocities,
+    calculate_pickup_ion_values,
+    calculate_helium_pui_temperature,
+    calculate_helium_pui_density,
+)
+from imap_l3_processing.swapi.constants import (
+    SWAPI_COARSE_SWEEP_BINS,
+    SWAPI_L2_K_FACTOR,
+)
 from imap_l3_processing.swapi.l3a.swapi_l3a_dependencies import SwapiL3ADependencies
-from imap_l3_processing.swapi.l3a.utils import chunk_l2_data
+from imap_l3_processing.swapi.l3a.utils import chunk_l2_data, rotate_rtn_to_dps
 from imap_l3_processing.swapi.l3b.models import SwapiL3BCombinedVDF
-from imap_l3_processing.swapi.l3b.science.calculate_solar_wind_differential_flux import \
-    calculate_combined_solar_wind_differential_flux
-from imap_l3_processing.swapi.l3b.science.calculate_solar_wind_vdf import calculate_proton_solar_wind_vdf, \
-    calculate_alpha_solar_wind_vdf, calculate_pui_solar_wind_vdf, calculate_delta_minus_plus
+from imap_l3_processing.swapi.l3b.science.calculate_solar_wind_differential_flux import (
+    calculate_combined_solar_wind_differential_flux,
+)
+from imap_l3_processing.swapi.l3b.science.calculate_solar_wind_vdf import (
+    calculate_proton_solar_wind_vdf,
+    calculate_alpha_solar_wind_vdf,
+    calculate_pui_solar_wind_vdf,
+    calculate_delta_minus_plus,
+)
 from imap_l3_processing.swapi.l3b.swapi_l3b_dependencies import SwapiL3BDependencies
 from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
 from imap_l3_processing.utils import save_data
 
 logger = logging.getLogger(__name__)
 
-MAXIMUM_ALLOWED_PROTON_SW_FITTING_CHI_SQ = 10
-
 
 class SwapiProcessor(Processor):
-    def __init__(self, dependencies: ProcessingInputCollection, input_metadata: InputMetadata):
+    def __init__(
+        self, dependencies: ProcessingInputCollection, input_metadata: InputMetadata
+    ):
         super().__init__(dependencies, input_metadata)
 
     def process(self):
         if self.input_metadata.data_level == "l3a":
-            l3a_dependencies = SwapiL3ADependencies.fetch_dependencies(self.dependencies)
+            l3a_dependencies = SwapiL3ADependencies.fetch_dependencies(
+                self.dependencies
+            )
 
-            if self.input_metadata.descriptor == 'proton-sw':
+            if self.input_metadata.descriptor == "proton-sw":
                 data = self.process_l3a_proton(l3a_dependencies.data, l3a_dependencies)
-            elif self.input_metadata.descriptor == 'alpha-sw':
-                data = self.process_l3a_alpha_solar_wind(l3a_dependencies.data, l3a_dependencies)
-            elif self.input_metadata.descriptor == 'pui-he':
+            elif self.input_metadata.descriptor == "alpha-sw":
+                data = self.process_l3a_alpha(l3a_dependencies.data, l3a_dependencies)
+            elif self.input_metadata.descriptor == "pui-he":
                 data = self.process_l3a_pui(l3a_dependencies.data, l3a_dependencies)
             else:
-                raise NotImplementedError("unknown descriptor", self.input_metadata.descriptor)
+                raise NotImplementedError(
+                    "unknown descriptor", self.input_metadata.descriptor
+                )
             data.parent_file_names = self.get_parent_file_names()
             cdf_path = save_data(data)
             return [cdf_path]
         elif self.input_metadata.data_level == "l3b":
-            l3b_dependencies = SwapiL3BDependencies.fetch_dependencies(self.dependencies)
+            l3b_dependencies = SwapiL3BDependencies.fetch_dependencies(
+                self.dependencies
+            )
             l3b_combined_vdf = self.process_l3b(l3b_dependencies.data, l3b_dependencies)
             l3b_combined_vdf.parent_file_names = self.get_parent_file_names()
             cdf_path = save_data(l3b_combined_vdf)
             return [cdf_path]
 
-    def process_l3a_pui(self, data, dependencies: SwapiL3ADependencies) -> SwapiL3PickupIonData:
-        proton_solar_wind_speeds = []
-        proton_solar_wind_clock_angles = []
-        proton_solar_wind_deflection_angles = []
-        proton_quality_flags = []
-
-        for data_chunk in chunk_l2_data(data, 5):
-            epoch = data_chunk.sci_start_time[0] + THIRTY_SECONDS_IN_NANOSECONDS
-            proton_solar_wind_speed = ufloat(np.nan, np.nan)
-            clock_angle = ufloat(np.nan, np.nan)
-            deflection_angle = ufloat(np.nan, np.nan)
-            quality_flag = SwapiL3Flags.NONE
-            try:
-                coincidence_count_rates_with_uncertainty = uarray(data_chunk.coincidence_count_rate,
-                                                                  data_chunk.coincidence_count_rate_uncertainty)
-                proton_solar_wind_speed, a, phi, b, chi_sq = calculate_proton_solar_wind_speed(
-                    coincidence_count_rates_with_uncertainty, data_chunk.energy, data_chunk.sci_start_time)
-                if chi_sq <= MAXIMUM_ALLOWED_PROTON_SW_FITTING_CHI_SQ:
-                    clock_angle = calculate_clock_angle(dependencies.clock_angle_and_flow_deflection_calibration_table,
-                                                        proton_solar_wind_speed, a, phi, b)
-                    deflection_angle = calculate_deflection_angle(
-                        dependencies.clock_angle_and_flow_deflection_calibration_table,
-                        proton_solar_wind_speed, a, phi, b)
-                else:
-                    deflection_angle, clock_angle = estimate_deflection_and_clock_angles(
-                        proton_solar_wind_speed.nominal_value)
-                    quality_flag |= SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED
-
-            except Exception as e:
-                logger.info(f"Exception occurred at epoch {epoch}, continuing with fill value", exc_info=True)
-            proton_solar_wind_speeds.append(proton_solar_wind_speed)
-            proton_solar_wind_clock_angles.append(clock_angle)
-            proton_solar_wind_deflection_angles.append(deflection_angle)
-            proton_quality_flags.append(quality_flag)
-
-        ten_minute_solar_wind_velocities, proton_sw_quality_flags = calculate_ten_minute_velocities(
-            nominal_values(proton_solar_wind_speeds),
-            nominal_values(proton_solar_wind_deflection_angles),
-            nominal_values(proton_solar_wind_clock_angles),
-            proton_quality_flags)
+    def process_l3a_proton(self, data, dependencies) -> SwapiL3ProtonSolarWindData:
+        chunks = list(chunk_l2_data(data, 5))
+        dependencies.swapi_response.warm_cache(data.energy / SWAPI_L2_K_FACTOR)
+        runner = ParallelChunkRunner(
+            dependencies.swapi_response, dependencies.efficiency_calibration_table
+        )
+
+        return SwapiL3ProtonSolarWindData(
+            replace(self.input_metadata, descriptor="proton-sw"),
+            **runner.run(chunks, ProtonChunkFitter()),
+        )
+
+    def process_l3a_alpha(self, data, dependencies) -> SwapiL3AlphaSolarWindData:
+        if dependencies.mag_data is None:
+            raise ValueError(
+                "alpha-sw requires MAG RTN data (L2 preferred, L1D fallback); "
+                "none was provided in the dependency collection."
+            )
+        chunks = list(chunk_l2_data(data, 5))
+        dependencies.swapi_response.warm_cache(data.energy / SWAPI_L2_K_FACTOR)
+        runner = ParallelChunkRunner(
+            dependencies.swapi_response, dependencies.efficiency_calibration_table
+        )
+
+        fitter = AlphaChunkFitter(dependencies.mag_data)
+        result = runner.run(chunks, fitter)
+        
+        if dependencies.mag_is_preliminary:
+            result["quality_flags"] = result["quality_flags"] | int(SwapiL3Flags.PRELIMINARY_MAG)
+        
+        metadata = replace(self.input_metadata, descriptor="alpha-sw")
+        return SwapiL3AlphaSolarWindData(metadata, **result)
+
+    def process_l3a_pui(
+        self, data, dependencies: SwapiL3ADependencies
+    ) -> SwapiL3PickupIonData:
+        chunks = list(chunk_l2_data(data, 5))
+        dependencies.swapi_response.warm_cache(data.energy / SWAPI_L2_K_FACTOR)
+        runner = ParallelChunkRunner(
+            dependencies.swapi_response, dependencies.efficiency_calibration_table
+        )
+
+        pui_proton_results = runner.run(chunks, ProtonChunkFitter())
+        chunk_velocities_dps = np.array([
+            rotate_rtn_to_dps(rtn_velocity, chunk_epoch)
+            for rtn_velocity, chunk_epoch in zip(
+                pui_proton_results["proton_sw_bulk_velocity_rtn_sc"],
+                pui_proton_results["epoch"],
+            )
+        ])
+        ten_minute_solar_wind_velocities, proton_sw_quality_flags = (
+            calculate_ten_minute_velocities(
+                chunk_velocities_dps,
+                list(pui_proton_results["quality_flags"]),
+            )
+        )
         pui_epochs = []
         pui_cooling_index = []
         pui_ionization_rate = []
@@ -114,7 +147,10 @@ def process_l3a_pui(self, data, dependencies: SwapiL3ADependencies) -> SwapiL3Pi
         pui_density = []
         pui_temperature = []
         bad_fit_flags = []
-        for data_chunk, sw_velocity in zip(chunk_l2_data(data, 50), ten_minute_solar_wind_velocities):
+
+        for data_chunk, sw_velocity in zip(
+            chunk_l2_data(data, 50), ten_minute_solar_wind_velocities
+        ):
             epoch = data_chunk.sci_start_time[0] + FIVE_MINUTES_IN_NANOSECONDS
             cooling_index = ufloat(np.nan, np.nan)
             ionization_rate = ufloat(np.nan, np.nan)
@@ -124,32 +160,46 @@ def process_l3a_pui(self, data, dependencies: SwapiL3ADependencies) -> SwapiL3Pi
             temperature = ufloat(np.nan, np.nan)
             bad_fit_flag = SwapiL3Flags.NONE
             try:
-                if (np.any(np.isnan(extract_coarse_sweep(data_chunk.coincidence_count_rate)))
-                        or np.any(np.isnan(sw_velocity))):
+                if np.any(
+                    np.isnan(data_chunk.coincidence_count_rate[:, SWAPI_COARSE_SWEEP_BINS])
+                ) or np.any(np.isnan(sw_velocity)):
                     raise ValueError("Fill values in input data")
-                fit_params = calculate_pickup_ion_values(dependencies.instrument_response_calibration_table,
-                                                         dependencies.geometric_factor_calibration_table,
-                                                         data_chunk.energy, data_chunk.coincidence_count_rate, epoch,
-                                                         sw_velocity,
-                                                         dependencies.density_of_neutral_helium_calibration_table,
-                                                         dependencies.efficiency_calibration_table,
-                                                         dependencies.hydrogen_inflow_vector,
-                                                         dependencies.helium_inflow_vector)
+                fit_params = calculate_pickup_ion_values(
+                    dependencies.instrument_response_calibration_table,
+                    dependencies.geometric_factor_calibration_table,
+                    data_chunk.energy,
+                    data_chunk.coincidence_count_rate,
+                    epoch,
+                    sw_velocity,
+                    dependencies.density_of_neutral_helium_calibration_table,
+                    dependencies.efficiency_calibration_table,
+                    dependencies.hydrogen_inflow_vector,
+                    dependencies.helium_inflow_vector,
+                )
                 cooling_index = fit_params.cooling_index
                 ionization_rate = fit_params.ionization_rate
                 cutoff_speed = fit_params.cutoff_speed
                 background_count_rate = fit_params.background_count_rate
-
                 bad_fit_flag |= fit_params.flags
-
                 density = calculate_helium_pui_density(
-                    epoch, sw_velocity, dependencies.density_of_neutral_helium_calibration_table, fit_params,
-                    dependencies.helium_inflow_vector)
+                    epoch,
+                    sw_velocity,
+                    dependencies.density_of_neutral_helium_calibration_table,
+                    fit_params,
+                    dependencies.helium_inflow_vector,
+                )
                 temperature = calculate_helium_pui_temperature(
-                    epoch, sw_velocity, dependencies.density_of_neutral_helium_calibration_table, fit_params,
-                    dependencies.helium_inflow_vector)
-            except Exception as e:
-                logger.info(f"Exception occurred at epoch {epoch}, continuing with fill value", exc_info=True)
+                    epoch,
+                    sw_velocity,
+                    dependencies.density_of_neutral_helium_calibration_table,
+                    fit_params,
+                    dependencies.helium_inflow_vector,
+                )
+            except Exception:
+                logger.info(
+                    f"Exception occurred at epoch {pycdf.lib.tt2000_to_datetime(int(epoch))}, continuing with fill value",
+                    exc_info=True,
+                )
             pui_epochs.append(epoch)
             pui_cooling_index.append(cooling_index)
             pui_ionization_rate.append(ionization_rate)
@@ -158,151 +208,17 @@ def process_l3a_pui(self, data, dependencies: SwapiL3ADependencies) -> SwapiL3Pi
             pui_density.append(density)
             pui_temperature.append(temperature)
             bad_fit_flags.append(bad_fit_flag)
-        pui_metadata = replace(self.input_metadata, descriptor="pui-he")
-        pui_data = SwapiL3PickupIonData(pui_metadata, np.array(pui_epochs), np.array(pui_cooling_index),
-                                        np.array(pui_ionization_rate),
-                                        np.array(pui_cutoff_speed), np.array(pui_background_rate),
-                                        np.array(pui_density), np.array(pui_temperature),
-                                        np.bitwise_or(proton_sw_quality_flags, bad_fit_flags))
-
-        return pui_data
-
-    def process_l3a_alpha_solar_wind(self, data, dependencies) -> SwapiL3AlphaSolarWindData:
-        epochs = []
-
-        alpha_solar_wind_speeds = []
-        alpha_solar_wind_densities = []
-        alpha_solar_wind_temperatures = []
-        alpha_solar_wind_bad_fit_flags = []
-        alpha_solar_wind_pre_lut_densities = []
-        alpha_solar_wind_pre_lut_temperatures = []
-
-        for data_chunk in chunk_l2_data(data, 5):
-            alpha_solar_wind_speed = ufloat(np.nan, np.nan)
-            alpha_density = ufloat(np.nan, np.nan)
-            alpha_temperature = ufloat(np.nan, np.nan)
-            alpha_pre_lut_density = ufloat(np.nan, np.nan)
-            alpha_pre_lut_temperature = ufloat(np.nan, np.nan)
-            epoch = data_chunk.sci_start_time[0] + THIRTY_SECONDS_IN_NANOSECONDS
-            bad_fit_flag = SwapiL3Flags.NONE
-            try:
-                if np.any(np.isnan(extract_coarse_sweep(data_chunk.coincidence_count_rate))):
-                    raise ValueError("Fill values in input data")
-                coincidence_count_rates_with_uncertainty = uarray(data_chunk.coincidence_count_rate,
-                                                                  data_chunk.coincidence_count_rate_uncertainty)
-
-                alpha_solar_wind_speed = calculate_alpha_solar_wind_speed(coincidence_count_rates_with_uncertainty,
-                                                                          data_chunk.energy)
-
-                alpha_temperature_density = calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps(
-                    dependencies.alpha_temperature_density_calibration_table, alpha_solar_wind_speed,
-                    coincidence_count_rates_with_uncertainty,
-                    data_chunk.energy, dependencies.efficiency_calibration_table.get_alpha_efficiency_for(epoch))
-
-                alpha_density = alpha_temperature_density.density
-                alpha_temperature = alpha_temperature_density.temperature
-                bad_fit_flag = alpha_temperature_density.bad_fit_flag
-                alpha_pre_lut_density = alpha_temperature_density.pre_lut_density
-                alpha_pre_lut_temperature = alpha_temperature_density.pre_lut_temperature
-
-            except Exception as e:
-                logger.info(f"Exception occurred at epoch {epoch}, continuing with fill value", exc_info=True)
-            finally:
-                epochs.append(epoch)
-                alpha_solar_wind_speeds.append(alpha_solar_wind_speed)
-                alpha_solar_wind_densities.append(alpha_density)
-                alpha_solar_wind_temperatures.append(alpha_temperature)
-                alpha_solar_wind_bad_fit_flags.append(bad_fit_flag)
-                alpha_solar_wind_pre_lut_densities.append(alpha_pre_lut_density)
-                alpha_solar_wind_pre_lut_temperatures.append(alpha_pre_lut_temperature)
-
-        alpha_solar_wind_speed_metadata = replace(self.input_metadata, descriptor="alpha-sw")
-        alpha_solar_wind_l3_data = SwapiL3AlphaSolarWindData(alpha_solar_wind_speed_metadata,
-                                                             np.array(epochs),
-                                                             np.array(alpha_solar_wind_speeds),
-                                                             np.array(alpha_solar_wind_temperatures),
-                                                             np.array(alpha_solar_wind_densities),
-                                                             np.array(alpha_solar_wind_bad_fit_flags),
-                                                             np.array(alpha_solar_wind_pre_lut_temperatures),
-                                                             np.array(alpha_solar_wind_pre_lut_densities),
-                                                             )
-        return alpha_solar_wind_l3_data
-
-    def process_l3a_proton(self, data, dependencies) -> SwapiL3ProtonSolarWindData:
-        epochs = []
-
-        proton_solar_wind_speeds = []
-        proton_solar_wind_temperatures = []
-        proton_solar_wind_density = []
-        proton_solar_wind_clock_angles = []
-        proton_solar_wind_deflection_angles = []
-        quality_flags = []
-
-        for data_chunk in chunk_l2_data(data, 5):
-            proton_solar_wind_speed = ufloat(np.nan, np.nan)
-            clock_angle = ufloat(np.nan, np.nan)
-            deflection_angle = ufloat(np.nan, np.nan)
-            proton_density = ufloat(np.nan, np.nan)
-            proton_temperature = ufloat(np.nan, np.nan)
-            quality_flag = SwapiL3Flags.NONE
-
-            epoch_center_of_chunk = data_chunk.sci_start_time[0] + THIRTY_SECONDS_IN_NANOSECONDS
-            try:
-                if np.any(np.isnan(extract_coarse_sweep(data_chunk.coincidence_count_rate))):
-                    raise ValueError("Fill values in input data")
-                coincidence_count_rates_with_uncertainty = uarray(data_chunk.coincidence_count_rate,
-                                                                  data_chunk.coincidence_count_rate_uncertainty)
-                proton_solar_wind_speed, a, phi, b, chi_sq = calculate_proton_solar_wind_speed(
-                    coincidence_count_rates_with_uncertainty, data_chunk.energy, data_chunk.sci_start_time)
-
-                if chi_sq <= MAXIMUM_ALLOWED_PROTON_SW_FITTING_CHI_SQ:
-                    clock_angle = calculate_clock_angle(dependencies.clock_angle_and_flow_deflection_calibration_table,
-                                                        proton_solar_wind_speed, a, phi, b)
-
-                    deflection_angle = calculate_deflection_angle(
-                        dependencies.clock_angle_and_flow_deflection_calibration_table,
-                        proton_solar_wind_speed, a, phi, b)
-                else:
-                    deflection_angle, clock_angle = estimate_deflection_and_clock_angles(
-                        proton_solar_wind_speed.nominal_value)
-                    quality_flag |= SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED
-                    deflection_angle = ufloat(deflection_angle, 45)
-                    clock_angle = ufloat(clock_angle, 180)
-
-                proton_temperature, proton_density, bad_fit_flag = astuple(
-                    calculate_proton_solar_wind_temperature_and_density(
-                        dependencies.proton_temperature_density_calibration_table,
-                        proton_solar_wind_speed,
-                        deflection_angle,
-                        clock_angle,
-                        coincidence_count_rates_with_uncertainty,
-                        data_chunk.energy,
-                        dependencies.efficiency_calibration_table.get_proton_efficiency_for(epoch_center_of_chunk)
-                    ))
-                quality_flag |= bad_fit_flag
-
-            except Exception as e:
-                epoch = epoch_center_of_chunk
-                logger.info(f"Exception occurred at epoch {epoch}, continuing with fill value", exc_info=True)
-
-            proton_solar_wind_speeds.append(proton_solar_wind_speed)
-            proton_solar_wind_clock_angles.append(clock_angle)
-            proton_solar_wind_deflection_angles.append(deflection_angle)
-            proton_solar_wind_density.append(proton_density)
-            proton_solar_wind_temperatures.append(proton_temperature)
-            epochs.append(epoch_center_of_chunk)
-            quality_flags.append(quality_flag)
-
-        proton_solar_wind_speed_metadata = replace(self.input_metadata, descriptor="proton-sw")
-        proton_solar_wind_l3_data = SwapiL3ProtonSolarWindData(proton_solar_wind_speed_metadata, np.array(epochs),
-                                                               np.array(proton_solar_wind_speeds),
-                                                               np.array(proton_solar_wind_temperatures),
-                                                               np.array(proton_solar_wind_density),
-                                                               np.array(proton_solar_wind_clock_angles),
-                                                               np.array(proton_solar_wind_deflection_angles),
-                                                               np.array(quality_flags))
-
-        return proton_solar_wind_l3_data
+        return SwapiL3PickupIonData(
+            replace(self.input_metadata, descriptor="pui-he"),
+            np.array(pui_epochs),
+            np.array(pui_cooling_index),
+            np.array(pui_ionization_rate),
+            np.array(pui_cutoff_speed),
+            np.array(pui_background_rate),
+            np.array(pui_density),
+            np.array(pui_temperature),
+            np.bitwise_or(proton_sw_quality_flags, bad_fit_flags),
+        )
 
     def process_l3b(self, data, dependencies):
         epochs = []
@@ -318,34 +234,47 @@ def process_l3b(self, data, dependencies):
         cdf_alpha_deltas = []
         cdf_pui_deltas = []
         combined_energy_deltas = []
+
         for data_chunk in chunk_l2_data(data, 50):
             center_of_epoch = data_chunk.sci_start_time[0] + FIVE_MINUTES_IN_NANOSECONDS
-            instrument_efficiency = dependencies.efficiency_calibration_table.get_proton_efficiency_for(center_of_epoch)
-            coincidence_count_rates_with_uncertainty = uarray(data_chunk.coincidence_count_rate,
-                                                              data_chunk.coincidence_count_rate_uncertainty)
-
-            average_coincident_count_rates, energies = calculate_combined_sweeps(
-                coincidence_count_rates_with_uncertainty, data_chunk.energy)
-
-            proton_velocities, proton_probabilities = calculate_proton_solar_wind_vdf(energies,
-                                                                                      average_coincident_count_rates,
-                                                                                      instrument_efficiency,
-                                                                                      dependencies.geometric_factor_calibration_table)
-
-            alpha_velocities, alpha_probabilities = calculate_alpha_solar_wind_vdf(energies,
-                                                                                   average_coincident_count_rates,
-                                                                                   instrument_efficiency,
-                                                                                   dependencies.geometric_factor_calibration_table)
-
-            pui_velocities, pui_probabilities = calculate_pui_solar_wind_vdf(energies,
-                                                                             average_coincident_count_rates,
-                                                                             instrument_efficiency,
-                                                                             dependencies.geometric_factor_calibration_table)
-            combined_differential_flux = calculate_combined_solar_wind_differential_flux(energies,
-                                                                                         average_coincident_count_rates,
-                                                                                         instrument_efficiency,
-                                                                                         dependencies.geometric_factor_calibration_table)
-
+            instrument_efficiency = (
+                dependencies.efficiency_calibration_table.get_proton_efficiency_for(
+                    center_of_epoch
+                )
+            )
+            coincidence_count_rates_with_uncertainty = uarray(
+                data_chunk.coincidence_count_rate,
+                data_chunk.coincidence_count_rate_uncertainty,
+            )
+            coarse_rates = coincidence_count_rates_with_uncertainty[:, SWAPI_COARSE_SWEEP_BINS]
+            average_coincident_count_rates = np.sum(coarse_rates, axis=0) / len(coarse_rates)
+            energies = np.mean(data_chunk.energy[:, SWAPI_COARSE_SWEEP_BINS], axis=0)
+            proton_velocities, proton_probabilities = calculate_proton_solar_wind_vdf(
+                energies,
+                average_coincident_count_rates,
+                instrument_efficiency,
+                dependencies.geometric_factor_calibration_table,
+            )
+            alpha_velocities, alpha_probabilities = calculate_alpha_solar_wind_vdf(
+                energies,
+                average_coincident_count_rates,
+                instrument_efficiency,
+                dependencies.geometric_factor_calibration_table,
+            )
+            pui_velocities, pui_probabilities = calculate_pui_solar_wind_vdf(
+                energies,
+                average_coincident_count_rates,
+                instrument_efficiency,
+                dependencies.geometric_factor_calibration_table,
+            )
+            combined_differential_flux = (
+                calculate_combined_solar_wind_differential_flux(
+                    energies,
+                    average_coincident_count_rates,
+                    instrument_efficiency,
+                    dependencies.geometric_factor_calibration_table,
+                )
+            )
             epochs.append(center_of_epoch)
             cdf_proton_velocities.append(proton_velocities)
             cdf_proton_probabilities.append(proton_probabilities)
@@ -364,32 +293,41 @@ def process_l3b(self, data, dependencies):
             combined_energy_deltas.append(calculate_delta_minus_plus(energies))
 
         l3b_combined_metadata = self.input_metadata
-        l3b_combined_metadata.descriptor = 'combined'
-        l3b_combined_vdf = SwapiL3BCombinedVDF(input_metadata=l3b_combined_metadata,
-                                               epoch=np.array(epochs),
-                                               proton_sw_velocities=np.array(cdf_proton_velocities),
-                                               proton_sw_velocities_delta_minus=np.array(
-                                                   [delta.delta_minus for delta in cdf_proton_deltas]),
-                                               proton_sw_velocities_delta_plus=np.array(
-                                                   [delta.delta_plus for delta in cdf_proton_deltas]),
-                                               proton_sw_combined_vdf=np.array(cdf_proton_probabilities),
-                                               alpha_sw_velocities=np.array(cdf_alpha_velocities),
-                                               alpha_sw_velocities_delta_minus=np.array(
-                                                   [delta.delta_minus for delta in cdf_alpha_deltas]),
-                                               alpha_sw_velocities_delta_plus=np.array(
-                                                   [delta.delta_plus for delta in cdf_alpha_deltas]),
-                                               alpha_sw_combined_vdf=np.array(cdf_alpha_probabilities),
-                                               pui_sw_velocities=np.array(cdf_pui_velocities),
-                                               pui_sw_velocities_delta_minus=np.array(
-                                                   [delta.delta_minus for delta in cdf_pui_deltas]),
-                                               pui_sw_velocities_delta_plus=np.array(
-                                                   [delta.delta_plus for delta in cdf_pui_deltas]),
-                                               pui_sw_combined_vdf=np.array(cdf_pui_probabilities),
-                                               combined_energy=np.array(combined_energies),
-                                               combined_energy_delta_minus=np.array(
-                                                   [delta.delta_minus for delta in combined_energy_deltas]),
-                                               combined_energy_delta_plus=np.array(
-                                                   [delta.delta_plus for delta in combined_energy_deltas]),
-                                               combined_differential_flux=np.array(combined_differential_fluxes),
-                                               )
+        l3b_combined_metadata.descriptor = "combined"
+        l3b_combined_vdf = SwapiL3BCombinedVDF(
+            input_metadata=l3b_combined_metadata,
+            epoch=np.array(epochs),
+            proton_sw_velocities=np.array(cdf_proton_velocities),
+            proton_sw_velocities_delta_minus=np.array(
+                [delta.delta_minus for delta in cdf_proton_deltas]
+            ),
+            proton_sw_velocities_delta_plus=np.array(
+                [delta.delta_plus for delta in cdf_proton_deltas]
+            ),
+            proton_sw_combined_vdf=np.array(cdf_proton_probabilities),
+            alpha_sw_velocities=np.array(cdf_alpha_velocities),
+            alpha_sw_velocities_delta_minus=np.array(
+                [delta.delta_minus for delta in cdf_alpha_deltas]
+            ),
+            alpha_sw_velocities_delta_plus=np.array(
+                [delta.delta_plus for delta in cdf_alpha_deltas]
+            ),
+            alpha_sw_combined_vdf=np.array(cdf_alpha_probabilities),
+            pui_sw_velocities=np.array(cdf_pui_velocities),
+            pui_sw_velocities_delta_minus=np.array(
+                [delta.delta_minus for delta in cdf_pui_deltas]
+            ),
+            pui_sw_velocities_delta_plus=np.array(
+                [delta.delta_plus for delta in cdf_pui_deltas]
+            ),
+            pui_sw_combined_vdf=np.array(cdf_pui_probabilities),
+            combined_energy=np.array(combined_energies),
+            combined_energy_delta_minus=np.array(
+                [delta.delta_minus for delta in combined_energy_deltas]
+            ),
+            combined_energy_delta_plus=np.array(
+                [delta.delta_plus for delta in combined_energy_deltas]
+            ),
+            combined_differential_flux=np.array(combined_differential_fluxes),
+        )
         return l3b_combined_vdf
diff --git a/imap_l3_processing/swe/l3/models.py b/imap_l3_processing/swe/l3/models.py
index 8e3455275..f85719a5e 100644
--- a/imap_l3_processing/swe/l3/models.py
+++ b/imap_l3_processing/swe/l3/models.py
@@ -115,9 +115,8 @@ class SweL2Data:
 class SwapiL3aProtonData:
     epoch: np.ndarray
     epoch_delta: np.ndarray
+    proton_sw_velocity_rtn: np.ndarray[float]
     proton_sw_speed: np.ndarray[float]
-    proton_sw_clock_angle: np.ndarray[float]
-    proton_sw_deflection_angle: np.ndarray[float]
     swp_flags: np.ndarray
 
 
diff --git a/imap_l3_processing/swe/l3/science/moment_calculations.py b/imap_l3_processing/swe/l3/science/moment_calculations.py
index e0de01fae..67a6e5d9b 100644
--- a/imap_l3_processing/swe/l3/science/moment_calculations.py
+++ b/imap_l3_processing/swe/l3/science/moment_calculations.py
@@ -384,6 +384,19 @@ def rotate_dps_vector_to_rtn(epoch: datetime, vector: np.ndarray) -> np.ndarray:
         return np.full(3, np.nan)
 
 
+def rotate_rtn_vectors_to_dps(epochs: np.ndarray, vectors_rtn: np.ndarray) -> np.ndarray:
+    result = np.full_like(vectors_rtn, np.nan, dtype=float)
+    for i, epoch in enumerate(epochs):
+        try:
+            et_time = spiceypy.datetime2et(epoch)
+            rotation_matrix = spiceypy.pxform("IMAP_RTN", "IMAP_DPS", et_time)
+        except spiceypy.SpiceyError as e:
+            logger.info(f"Failed to rotate RTN→DPS at epoch {epoch}: {e}")
+            continue
+        result[i] = rotation_matrix @ vectors_rtn[i]
+    return result
+
+
 def rotate_temperature(epoch: datetime, alpha: float, beta: float) -> tuple[float, float]:
     sin_dec = np.sin(beta)
     x = sin_dec * np.cos(alpha)
diff --git a/imap_l3_processing/swe/l3/swe_l3_dependencies.py b/imap_l3_processing/swe/l3/swe_l3_dependencies.py
index 28736ba7e..1de137846 100644
--- a/imap_l3_processing/swe/l3/swe_l3_dependencies.py
+++ b/imap_l3_processing/swe/l3/swe_l3_dependencies.py
@@ -10,7 +10,7 @@
 from imap_l3_processing.swe.l3.models import SweL2Data, SweConfiguration, SwapiL3aProtonData, SweL1bData
 from imap_l3_processing.swe.l3.utils import read_l2_swe_data, read_l3a_swapi_proton_data, read_swe_config, \
     read_l1b_swe_data
-from imap_l3_processing.utils import read_mag_data
+from imap_l3_processing.utils import read_mag_data, select_mag_path
 
 MAG_DESPUN_L1D_DESCRIPTOR = "norm-dsrf"
 SWAPI_L3A_PROTON_DESCRIPTOR = "proton-sw"
@@ -41,21 +41,10 @@ def fetch_dependencies(cls, dependencies: ProcessingInputCollection) -> SweL3Dep
                 d.imap_file_paths[0] for d in science_files if d.source == "swe" and d.data_type == "l1b")
         except StopIteration:
             raise ValueError(f"Missing SWE l1b dependency.")
-        mag_dependency = next(
-            (d.imap_file_paths[0] for d in science_files if d.source == "mag"
-            and d.descriptor == MAG_DESPUN_L1D_DESCRIPTOR
-            and d.data_type == "l2"), None)
-        mag_is_preliminary = False
-        if mag_dependency is None:
-            try:
-                mag_dependency = next(
-                    d.imap_file_paths[0] for d in science_files if d.source == "mag"
-                    and d.descriptor == MAG_DESPUN_L1D_DESCRIPTOR
-                    and d.data_type == "l1d"
-                )
-                mag_is_preliminary = True
-            except StopIteration:
-                raise ValueError(f"Missing MAG {MAG_DESPUN_L1D_DESCRIPTOR} dependency.")
+        mag_file, mag_level = select_mag_path(dependencies, MAG_DESPUN_L1D_DESCRIPTOR)
+        if mag_file is None:
+            raise ValueError(f"Missing MAG {MAG_DESPUN_L1D_DESCRIPTOR} dependency.")
+        mag_is_preliminary = mag_level == "l1d"
         try:
             swapi_dependency = next(
                 d.imap_file_paths[0] for d in science_files if d.source == "swapi"
@@ -65,7 +54,6 @@ def fetch_dependencies(cls, dependencies: ProcessingInputCollection) -> SweL3Dep
 
         swe_l2_file = download(swe_l2_dependency.construct_path())
         swe_l1b_file = download(swe_l1b_dependency.construct_path())
-        mag_file = download(mag_dependency.construct_path())
         swapi_file = download(swapi_dependency.construct_path())
         swe_config = download(swe_config_dependency)
 
diff --git a/imap_l3_processing/swe/l3/utils.py b/imap_l3_processing/swe/l3/utils.py
index 628102628..43f11352c 100644
--- a/imap_l3_processing/swe/l3/utils.py
+++ b/imap_l3_processing/swe/l3/utils.py
@@ -55,15 +55,16 @@ def read_l3a_swapi_proton_data(swapi_l3a_data: Path) -> SwapiL3aProtonData:
         else:
             epoch_delta = np.repeat(timedelta(seconds=epoch_delta_ns), len(epoch))
         proton_sw_speed = read_numeric_variable(cdf["proton_sw_speed"])
-        proton_sw_clock_angle = read_numeric_variable(cdf["proton_sw_clock_angle"])
-        proton_sw_deflection_angle = read_numeric_variable(cdf["proton_sw_deflection_angle"])
+        if "proton_sw_bulk_velocity_rtn_sc" in cdf:
+            proton_sw_velocity_rtn = read_numeric_variable(cdf["proton_sw_bulk_velocity_rtn_sc"])
+        else:
+            proton_sw_velocity_rtn = np.full((len(epoch), 3), np.nan)
         swp_flags = cdf["swp_flags"][...]
 
     return SwapiL3aProtonData(epoch=epoch,
                               epoch_delta=epoch_delta,
+                              proton_sw_velocity_rtn=proton_sw_velocity_rtn,
                               proton_sw_speed=proton_sw_speed,
-                              proton_sw_clock_angle=proton_sw_clock_angle,
-                              proton_sw_deflection_angle=proton_sw_deflection_angle,
                               swp_flags=swp_flags)
 
 
diff --git a/imap_l3_processing/swe/swe_processor.py b/imap_l3_processing/swe/swe_processor.py
index 2d5ca4372..ae245f87d 100644
--- a/imap_l3_processing/swe/swe_processor.py
+++ b/imap_l3_processing/swe/swe_processor.py
@@ -8,19 +8,38 @@
 from imap_l3_processing.data_utils import find_closest_neighbor
 from imap_l3_processing.models import InputMetadata
 from imap_l3_processing.processor import Processor
-from imap_l3_processing.swapi.l3a.science.calculate_pickup_ion import calculate_solar_wind_velocity_vector
-from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
-from imap_l3_processing.swe.l3.models import SweL3Data, SweL1bData, SweL2Data, SweL3MomentData, SweConfiguration
-from imap_l3_processing.swe.l3.science.moment_calculations import compute_maxwellian_weight_factors, \
-    rotate_temperature, \
-    rotate_dps_vector_to_rtn, core_fit_moments_retrying_on_failure, halo_fit_moments_retrying_on_failure, Moments, \
-    integrate, scale_core_density, scale_halo_density, rotate_vector_to_rtn_spherical_coordinates, \
-    calculate_primary_eigenvector, \
-    ScaleDensityOutput, rotate_temperature_tensor_to_mag
-from imap_l3_processing.swe.l3.science.pitch_calculations import average_over_look_directions, mec_breakpoint_finder, \
-    correct_and_rebin, \
-    integrate_distribution_to_get_1d_spectrum, integrate_distribution_to_get_inbound_and_outbound_1d_spectrum, \
-    calculate_velocity_in_dsp_frame_km_s, swe_rebin_intensity_by_pitch_angle_and_gyrophase
+from imap_l3_processing.swe.l3.models import (
+    SweL3Data,
+    SweL1bData,
+    SweL2Data,
+    SweL3MomentData,
+    SweConfiguration,
+)
+from imap_l3_processing.swe.l3.science.moment_calculations import (
+    compute_maxwellian_weight_factors,
+    rotate_temperature,
+    rotate_dps_vector_to_rtn,
+    rotate_rtn_vectors_to_dps,
+    core_fit_moments_retrying_on_failure,
+    halo_fit_moments_retrying_on_failure,
+    Moments,
+    integrate,
+    scale_core_density,
+    scale_halo_density,
+    rotate_vector_to_rtn_spherical_coordinates,
+    calculate_primary_eigenvector,
+    ScaleDensityOutput,
+    rotate_temperature_tensor_to_mag,
+)
+from imap_l3_processing.swe.l3.science.pitch_calculations import (
+    average_over_look_directions,
+    mec_breakpoint_finder,
+    correct_and_rebin,
+    integrate_distribution_to_get_1d_spectrum,
+    integrate_distribution_to_get_inbound_and_outbound_1d_spectrum,
+    calculate_velocity_in_dsp_frame_km_s,
+    swe_rebin_intensity_by_pitch_angle_and_gyrophase,
+)
 from imap_l3_processing.swe.l3.swe_l3_dependencies import SweL3Dependencies
 from imap_l3_processing.swe.l3.utils import compute_epoch_delta_in_ns
 from imap_l3_processing.swe.quality_flags import SweL3Flags
@@ -77,7 +96,9 @@ def check_temperature_outlier_flag(data: np.ndarray):
 
 
 class SweProcessor(Processor):
-    def __init__(self, dependencies: ProcessingInputCollection, input_metadata: InputMetadata):
+    def __init__(
+        self, dependencies: ProcessingInputCollection, input_metadata: InputMetadata
+    ):
         super().__init__(dependencies, input_metadata)
 
     def process(self):
@@ -90,42 +111,57 @@ def process(self):
     def calculate_products(self, dependencies: SweL3Dependencies) -> SweL3Data:
         swe_l2_data = dependencies.swe_l2_data
         swe_epoch = swe_l2_data.epoch
-        epoch_delta = compute_epoch_delta_in_ns(swe_l2_data.acquisition_duration,
-                                                dependencies.swe_l1b_data.settle_duration)
+        epoch_delta = compute_epoch_delta_in_ns(
+            swe_l2_data.acquisition_duration, dependencies.swe_l1b_data.settle_duration
+        )
         config = dependencies.configuration
 
         average_psd = []
         swe_quality_flags = np.empty_like(swe_epoch, dtype=np.uint16)
-        spacecraft_potential: np.ndarray[np.float64] = np.empty_like(swe_epoch, dtype=np.float64)
+        spacecraft_potential: np.ndarray[np.float64] = np.empty_like(
+            swe_epoch, dtype=np.float64
+        )
         halo_core: np.ndarray[np.float64] = np.empty_like(swe_epoch, dtype=np.float64)
         corrected_energy_bins = []
-        rebinned_mag_data = dependencies.mag_data.rebin_to(swe_epoch,
-                                                           [datetime.timedelta(seconds=delta / 1e9)
-                                                                for delta
-                                                                in epoch_delta])
+        rebinned_mag_data = dependencies.mag_data.rebin_to(
+            swe_epoch,
+            [datetime.timedelta(seconds=delta / 1e9) for delta in epoch_delta],
+        )
 
         geometric_fractions = np.array(config["geometric_fractions"])
         for i in range(len(swe_epoch)):
             npts = 7 // 2
             left_idx = 0 if i - npts < 0 else i - npts
             right_idx = i + (i - left_idx + 1)
-            time_avg_psd = np.nanmean(swe_l2_data.phase_space_density[left_idx:right_idx], axis=0)
-
-            average_psd.append(average_over_look_directions(time_avg_psd,
-                                                            geometric_fractions,
-                                                            config["minimum_phase_space_density_value"]))
+            time_avg_psd = np.nanmean(
+                swe_l2_data.phase_space_density[left_idx:right_idx], axis=0
+            )
+
+            average_psd.append(
+                average_over_look_directions(
+                    time_avg_psd,
+                    geometric_fractions,
+                    config["minimum_phase_space_density_value"],
+                )
+            )
 
-            spacecraft_potential[i], halo_core[i], swe_quality_flags[i] = mec_breakpoint_finder(swe_l2_data.energy,
-                                                                                                   average_psd[i])
+            spacecraft_potential[i], halo_core[i], swe_quality_flags[i] = (
+                mec_breakpoint_finder(swe_l2_data.energy, average_psd[i])
+            )
 
             corrected_energy_bins.append(swe_l2_data.energy - spacecraft_potential[i])
 
         corrected_energy_bins = np.array(corrected_energy_bins)
 
-        swe_l3_moments_data = self.calculate_moment_products(swe_l2_data, dependencies.swe_l1b_data,
-                                                             rebinned_mag_data,
-                                                             spacecraft_potential, halo_core,
-                                                             corrected_energy_bins, config)
+        swe_l3_moments_data = self.calculate_moment_products(
+            swe_l2_data,
+            dependencies.swe_l1b_data,
+            rebinned_mag_data,
+            spacecraft_potential,
+            halo_core,
+            corrected_energy_bins,
+            config,
+        )
         # Check Temperature Outlier Flags and add to swe_quality_flags
         # each temperature variable needs checked
         temperature_outlier_flags = check_temperature_outlier_flag(swe_l3_moments_data.core_t_parallel_integrated)
@@ -146,10 +182,16 @@ def calculate_products(self, dependencies: SweL3Dependencies) -> SweL3Data:
         swe_quality_flags |= temperature_outlier_flags
 
         (
-            phase_space_density_by_pitch_angle, phase_space_density_by_pitch_angle_and_gyrophase,
-            energy_spectrum, energy_spectrum_inbound, energy_spectrum_outbound,
-            intensity_by_pitch_angle_and_gyrophase, intensity_by_pitch_angle,
-            uncertanties_by_pitch_angle_and_gyrophase, uncertanties_by_pitch_angle, pitch_angle_flags
+            phase_space_density_by_pitch_angle,
+            phase_space_density_by_pitch_angle_and_gyrophase,
+            energy_spectrum,
+            energy_spectrum_inbound,
+            energy_spectrum_outbound,
+            intensity_by_pitch_angle_and_gyrophase,
+            intensity_by_pitch_angle,
+            uncertanties_by_pitch_angle_and_gyrophase,
+            uncertanties_by_pitch_angle,
+            pitch_angle_flags,
         ) = self.calculate_pitch_angle_products(dependencies, corrected_energy_bins)
 
         swe_quality_flags |= pitch_angle_flags
@@ -158,7 +200,9 @@ def calculate_products(self, dependencies: SweL3Dependencies) -> SweL3Data:
             swe_quality_flags = np.bitwise_or(swe_quality_flags, SweL3Flags.PRELIMINARY_MAG)
 
         rebinned_mask = np.ma.masked_invalid(swe_l2_data.phase_space_density_rebinned)
-        dist_by_phi_rebinned = np.average(rebinned_mask, weights=geometric_fractions, axis=-1)
+        dist_by_phi_rebinned = np.average(
+            rebinned_mask, weights=geometric_fractions, axis=-1
+        )
         dist_fun_1d_rebinned = np.ma.average(dist_by_phi_rebinned, axis=-1)
         dist_by_theta_rebinned = np.ma.average(rebinned_mask, axis=-2)
 
@@ -185,21 +229,59 @@ def calculate_products(self, dependencies: SweL3Dependencies) -> SweL3Data:
             phase_space_density_inward=energy_spectrum_inbound,
             phase_space_density_outward=energy_spectrum_outbound,
             moment_data=swe_l3_moments_data,
-            phi=np.array([6., 18., 30., 42., 54., 66., 78., 90., 102., 114., 126.,
-                          138., 150., 162., 174., 186., 198., 210., 222., 234., 246., 258.,
-                          270., 282., 294., 306., 318., 330., 342., 354.], dtype=np.float32),
+            phi=np.array(
+                [
+                    6.0,
+                    18.0,
+                    30.0,
+                    42.0,
+                    54.0,
+                    66.0,
+                    78.0,
+                    90.0,
+                    102.0,
+                    114.0,
+                    126.0,
+                    138.0,
+                    150.0,
+                    162.0,
+                    174.0,
+                    186.0,
+                    198.0,
+                    210.0,
+                    222.0,
+                    234.0,
+                    246.0,
+                    258.0,
+                    270.0,
+                    282.0,
+                    294.0,
+                    306.0,
+                    318.0,
+                    330.0,
+                    342.0,
+                    354.0,
+                ],
+                dtype=np.float32,
+            ),
             # Our PSD input is not rebinned to a consistent spin angle, it's by "spin sector". Do we need to do this calculation using a different input var that has consistent angle?
             theta=swe_l2_data.inst_el,
-
             raw_1d_psd_rebinned=dist_fun_1d_rebinned,
             raw_psd_by_phi_rebinned=dist_by_phi_rebinned,
             raw_psd_by_theta_rebinned=dist_by_theta_rebinned,
-            swe_flags=swe_quality_flags
+            swe_flags=swe_quality_flags,
         )
 
-    def calculate_moment_products(self, swe_l2_data: SweL2Data, swe_l1b_data: SweL1bData, rebinned_mag_data: np.ndarray,
-                                  spacecraft_potential: np.ndarray, halo_core: np.ndarray,
-                                  corrected_energy_bins: np.ndarray, config: SweConfiguration) -> SweL3MomentData:
+    def calculate_moment_products(
+        self,
+        swe_l2_data: SweL2Data,
+        swe_l1b_data: SweL1bData,
+        rebinned_mag_data: np.ndarray,
+        spacecraft_potential: np.ndarray,
+        halo_core: np.ndarray,
+        corrected_energy_bins: np.ndarray,
+        config: SweConfiguration,
+    ) -> SweL3MomentData:
         number_of_points = len(swe_l2_data.epoch)
         core_density_history = [100 for _ in range(3)]
         halo_density_history = [25 for _ in range(3)]
@@ -256,24 +338,43 @@ def calculate_moment_products(self, swe_l2_data: SweL2Data, swe_l1b_data: SweL1b
 
         for i in range(len(swe_l2_data.epoch)):
             try:
-                velocity_vectors_cm_per_s: np.ndarray = 1000 * 100 * calculate_velocity_in_dsp_frame_km_s(
-                    corrected_energy_bins[i],
-                    swe_l2_data.inst_el,
-                    swe_l2_data.inst_az_spin_sector[i])
+                velocity_vectors_cm_per_s: np.ndarray = (
+                    1000
+                    * 100
+                    * calculate_velocity_in_dsp_frame_km_s(
+                        corrected_energy_bins[i],
+                        swe_l2_data.inst_el,
+                        swe_l2_data.inst_az_spin_sector[i],
+                    )
+                )
 
-                weights: np.ndarray[float] = compute_maxwellian_weight_factors(swe_l1b_data.count_rates[i],
-                                                                               swe_l2_data.acquisition_duration[
-                                                                                   i] / 1e6)
+                weights: np.ndarray[float] = compute_maxwellian_weight_factors(
+                    swe_l1b_data.count_rates[i],
+                    swe_l2_data.acquisition_duration[i] / 1e6,
+                )
                 ifit = next(
-                    index for index, energy in enumerate(swe_l2_data.energy) if energy >= spacecraft_potential[i])
-                jbreak = next(index for index, energy in enumerate(swe_l2_data.energy) if energy >= halo_core[i])
+                    index
+                    for index, energy in enumerate(swe_l2_data.energy)
+                    if energy >= spacecraft_potential[i]
+                )
+                jbreak = next(
+                    index
+                    for index, energy in enumerate(swe_l2_data.energy)
+                    if energy >= halo_core[i]
+                )
                 core_nfit = jbreak - ifit
                 ifit += 1
                 if core_nfit == 0:
-                    logger.info(f"Bad core-halo breakpoint value at index {i}. Continuing.")
+                    logger.info(
+                        f"Bad core-halo breakpoint value at index {i}. Continuing."
+                    )
                     continue
 
-                halo_nfit = 5 if len(swe_l2_data.energy) - jbreak > 5 else len(swe_l2_data.energy) - jbreak
+                halo_nfit = (
+                    5
+                    if len(swe_l2_data.energy) - jbreak > 5
+                    else len(swe_l2_data.energy) - jbreak
+                )
 
                 core_moment_fit_result = core_fit_moments_retrying_on_failure(
                     corrected_energy_bins[i],
@@ -282,7 +383,7 @@ def calculate_moment_products(self, swe_l2_data: SweL2Data, swe_l1b_data: SweL1b
                     weights,
                     ifit,
                     ifit + core_nfit,
-                    core_density_history
+                    core_density_history,
                 )
 
                 current_epoch = swe_l2_data.epoch[i]
@@ -292,117 +393,198 @@ def calculate_moment_products(self, swe_l2_data: SweL2Data, swe_l1b_data: SweL1b
                     core_fit_chi_squareds[i] = core_moment_fit_result.chisq
                     core_fit_num_points[i] = core_moment_fit_result.number_of_points
 
-                    core_density_history = [*core_density_history[1:], core_moment.density]
-
-                    core_rtn_velocity[i] = rotate_dps_vector_to_rtn(current_epoch,
-                                                                    np.array(
-                                                                        [core_moment.velocity_x, core_moment.velocity_y,
-                                                                         core_moment.velocity_z]))
-
-                    core_temp_theta_rtn, core_temp_phi_rtn = rotate_temperature(current_epoch, core_moment.alpha,
-                                                                                core_moment.beta)
+                    core_density_history = [
+                        *core_density_history[1:],
+                        core_moment.density,
+                    ]
+
+                    core_rtn_velocity[i] = rotate_dps_vector_to_rtn(
+                        current_epoch,
+                        np.array(
+                            [
+                                core_moment.velocity_x,
+                                core_moment.velocity_y,
+                                core_moment.velocity_z,
+                            ]
+                        ),
+                    )
+
+                    core_temp_theta_rtn, core_temp_phi_rtn = rotate_temperature(
+                        current_epoch, core_moment.alpha, core_moment.beta
+                    )
                     core_temp_theta_rtns[i] = core_temp_theta_rtn
                     core_temp_phi_rtns[i] = core_temp_phi_rtn
-                    core_temp_avg = (2 * core_moment.t_perpendicular + core_moment.t_parallel) / 3
+                    core_temp_avg = (
+                        2 * core_moment.t_perpendicular + core_moment.t_parallel
+                    ) / 3
 
                     if 1e3 < core_temp_avg < 1e7:
-                        core_integrate_result = integrate(ifit + 1, jbreak - 1, corrected_energy_bins[i],
-                                                          sin_theta, cos_theta,
-                                                          config['aperture_field_of_view_radians'],
-                                                          swe_l2_data.phase_space_density[i],
-                                                          instrument_phi[i],
-                                                          spacecraft_potential[i],
-                                                          [0, 0, 0, 0], [0, 0, 0, 0, 0, 0])
+                        core_integrate_result = integrate(
+                            ifit + 1,
+                            jbreak - 1,
+                            corrected_energy_bins[i],
+                            sin_theta,
+                            cos_theta,
+                            config["aperture_field_of_view_radians"],
+                            swe_l2_data.phase_space_density[i],
+                            instrument_phi[i],
+                            spacecraft_potential[i],
+                            [0, 0, 0, 0],
+                            [0, 0, 0, 0, 0, 0],
+                        )
                         if core_integrate_result is not None:
-                            scale_core_density_output: ScaleDensityOutput = scale_core_density(
-                                core_integrate_result.density,
-                                core_integrate_result.velocity,
-                                core_integrate_result.temperature, core_moment,
-                                ifit,
-                                corrected_energy_bins[i],
-                                spacecraft_potential[i], cos_theta,
-                                config['aperture_field_of_view_radians'],
-                                instrument_phi[i],
-                                core_moment_fit_result.regress_result,
-                                core_integrate_result.base_energy)
-
-                            core_density_integrated[i] = scale_core_density_output.density
-                            core_velocity_integrated[i] = rotate_dps_vector_to_rtn(current_epoch,
-                                                                                   scale_core_density_output.velocity)
-                            core_temperature_tensor_integrated[i] = scale_core_density_output.temperature
-
-                            magnitude, theta, phi = rotate_vector_to_rtn_spherical_coordinates(current_epoch,
-                                                                                               core_integrate_result.heat_flux)
+                            scale_core_density_output: ScaleDensityOutput = (
+                                scale_core_density(
+                                    core_integrate_result.density,
+                                    core_integrate_result.velocity,
+                                    core_integrate_result.temperature,
+                                    core_moment,
+                                    ifit,
+                                    corrected_energy_bins[i],
+                                    spacecraft_potential[i],
+                                    cos_theta,
+                                    config["aperture_field_of_view_radians"],
+                                    instrument_phi[i],
+                                    core_moment_fit_result.regress_result,
+                                    core_integrate_result.base_energy,
+                                )
+                            )
+
+                            core_density_integrated[i] = (
+                                scale_core_density_output.density
+                            )
+                            core_velocity_integrated[i] = rotate_dps_vector_to_rtn(
+                                current_epoch, scale_core_density_output.velocity
+                            )
+                            core_temperature_tensor_integrated[i] = (
+                                scale_core_density_output.temperature
+                            )
+
+                            magnitude, theta, phi = (
+                                rotate_vector_to_rtn_spherical_coordinates(
+                                    current_epoch, core_integrate_result.heat_flux
+                                )
+                            )
 
                             core_heat_flux_magnitude[i] = magnitude
                             core_heat_flux_theta[i] = theta
                             core_heat_flux_phi[i] = phi
 
-                            core_primary_eigen_vector, core_temps = calculate_primary_eigenvector(
-                                scale_core_density_output.temperature)
+                            core_primary_eigen_vector, core_temps = (
+                                calculate_primary_eigenvector(
+                                    scale_core_density_output.temperature
+                                )
+                            )
 
                             core_t_parallel_integrated[i] = core_temps[0]
-                            core_t_perpendicular_integrated[i] = [core_temps[1], core_temps[2]]
-
-                            magnitude, theta, phi = rotate_vector_to_rtn_spherical_coordinates(current_epoch,
-                                                                                               core_primary_eigen_vector)
+                            core_t_perpendicular_integrated[i] = [
+                                core_temps[1],
+                                core_temps[2],
+                            ]
+
+                            magnitude, theta, phi = (
+                                rotate_vector_to_rtn_spherical_coordinates(
+                                    current_epoch, core_primary_eigen_vector
+                                )
+                            )
                             core_temperature_theta_rtn_integrated[i] = theta
                             core_temperature_phi_rtn_integrated[i] = phi
 
-                            core_t_parallel_to_mag, core_t_perpendicular_to_mag_average, core_t_perpendicular_to_mag_ratio = rotate_temperature_tensor_to_mag(
-                                scale_core_density_output.temperature, rebinned_mag_data[i])
+                            (
+                                core_t_parallel_to_mag,
+                                core_t_perpendicular_to_mag_average,
+                                core_t_perpendicular_to_mag_ratio,
+                            ) = rotate_temperature_tensor_to_mag(
+                                scale_core_density_output.temperature,
+                                rebinned_mag_data[i],
+                            )
 
                             core_temperature_parallel_to_mag[i] = core_t_parallel_to_mag
-                            core_temperature_perpendicular_to_mag[i] = [core_t_perpendicular_to_mag_average,
-                                                                        core_t_perpendicular_to_mag_ratio]
-
-                            total_integration_output = integrate(ifit + 1,
-                                                                 len(corrected_energy_bins[i]) - 6,
-                                                                 corrected_energy_bins[i],
-                                                                 sin_theta,
-                                                                 cos_theta, config['aperture_field_of_view_radians'],
-                                                                 swe_l2_data.phase_space_density[i],
-                                                                 instrument_phi[i],
-                                                                 spacecraft_potential[i],
-                                                                 scale_core_density_output.cdelnv,
-                                                                 scale_core_density_output.cdelt)
-                            assert total_integration_output is not None, "not yet checking if this is None"
-                            total_density_integrated[i] = total_integration_output.density
-                            total_velocity_integrated[i] = rotate_dps_vector_to_rtn(current_epoch,
-                                                                                    total_integration_output.velocity)
-                            total_temperature_tensor_integrated[i] = total_integration_output.temperature
-
-                            magnitude, theta, phi = rotate_vector_to_rtn_spherical_coordinates(
-                                current_epoch,
-                                total_integration_output.heat_flux)
+                            core_temperature_perpendicular_to_mag[i] = [
+                                core_t_perpendicular_to_mag_average,
+                                core_t_perpendicular_to_mag_ratio,
+                            ]
+
+                            total_integration_output = integrate(
+                                ifit + 1,
+                                len(corrected_energy_bins[i]) - 6,
+                                corrected_energy_bins[i],
+                                sin_theta,
+                                cos_theta,
+                                config["aperture_field_of_view_radians"],
+                                swe_l2_data.phase_space_density[i],
+                                instrument_phi[i],
+                                spacecraft_potential[i],
+                                scale_core_density_output.cdelnv,
+                                scale_core_density_output.cdelt,
+                            )
+                            assert total_integration_output is not None, (
+                                "not yet checking if this is None"
+                            )
+                            total_density_integrated[i] = (
+                                total_integration_output.density
+                            )
+                            total_velocity_integrated[i] = rotate_dps_vector_to_rtn(
+                                current_epoch, total_integration_output.velocity
+                            )
+                            total_temperature_tensor_integrated[i] = (
+                                total_integration_output.temperature
+                            )
+
+                            magnitude, theta, phi = (
+                                rotate_vector_to_rtn_spherical_coordinates(
+                                    current_epoch, total_integration_output.heat_flux
+                                )
+                            )
 
                             total_heat_flux_magnitude[i] = magnitude
                             total_heat_flux_theta[i] = theta
                             total_heat_flux_phi[i] = phi
 
-                            total_primary_eigen_vector, total_temps = calculate_primary_eigenvector(
-                                total_integration_output.temperature)
+                            total_primary_eigen_vector, total_temps = (
+                                calculate_primary_eigenvector(
+                                    total_integration_output.temperature
+                                )
+                            )
 
                             total_t_parallel_integrated[i] = total_temps[0]
-                            total_t_perpendicular_integrated[i] = [total_temps[1], total_temps[2]]
-
-                            magnitude, theta, phi = rotate_vector_to_rtn_spherical_coordinates(current_epoch,
-                                                                                               total_primary_eigen_vector)
+                            total_t_perpendicular_integrated[i] = [
+                                total_temps[1],
+                                total_temps[2],
+                            ]
+
+                            magnitude, theta, phi = (
+                                rotate_vector_to_rtn_spherical_coordinates(
+                                    current_epoch, total_primary_eigen_vector
+                                )
+                            )
                             total_temperature_theta_rtn_integrated[i] = theta
                             total_temperature_phi_rtn_integrated[i] = phi
 
-                            total_t_parallel_to_mag, total_t_perpendicular_to_mag_average, total_t_perpendicular_to_mag_ratio = rotate_temperature_tensor_to_mag(
-                                total_integration_output.temperature, rebinned_mag_data[i])
-
-                            total_temperature_parallel_to_mag[i] = total_t_parallel_to_mag
-                            total_temperature_perpendicular_to_mag[i] = [total_t_perpendicular_to_mag_average,
-                                                                         total_t_perpendicular_to_mag_ratio]
+                            (
+                                total_t_parallel_to_mag,
+                                total_t_perpendicular_to_mag_average,
+                                total_t_perpendicular_to_mag_ratio,
+                            ) = rotate_temperature_tensor_to_mag(
+                                total_integration_output.temperature,
+                                rebinned_mag_data[i],
+                            )
+
+                            total_temperature_parallel_to_mag[i] = (
+                                total_t_parallel_to_mag
+                            )
+                            total_temperature_perpendicular_to_mag[i] = [
+                                total_t_perpendicular_to_mag_average,
+                                total_t_perpendicular_to_mag_ratio,
+                            ]
 
                         else:
                             logger.info(f"core integrate failed at index {i}")
 
                     else:
-                        logger.info(f"core temp {core_temp_avg} out of range at index {i}")
+                        logger.info(
+                            f"core temp {core_temp_avg} out of range at index {i}"
+                        )
                 else:
                     logger.info(f"bad core moment fit result at index {i}")
 
@@ -423,91 +605,153 @@ def calculate_moment_products(self, swe_l2_data: SweL2Data, swe_l1b_data: SweL1b
                     halo_moments[i] = halo_moment
                     halo_fit_chi_squareds[i] = halo_moment_fit_result.chisq
 
-                    halo_density_history = [*halo_density_history[1:], halo_moment.density]
-
-                    halo_rtn_velocity[i] = rotate_dps_vector_to_rtn(current_epoch,
-                                                                    np.array(
-                                                                        [halo_moment.velocity_x, halo_moment.velocity_y,
-                                                                         halo_moment.velocity_z]))
-
-                    halo_temp_theta_rtn, halo_temp_phi_rtn = rotate_temperature(current_epoch, halo_moment.alpha,
-                                                                                halo_moment.beta)
+                    halo_density_history = [
+                        *halo_density_history[1:],
+                        halo_moment.density,
+                    ]
+
+                    halo_rtn_velocity[i] = rotate_dps_vector_to_rtn(
+                        current_epoch,
+                        np.array(
+                            [
+                                halo_moment.velocity_x,
+                                halo_moment.velocity_y,
+                                halo_moment.velocity_z,
+                            ]
+                        ),
+                    )
+
+                    halo_temp_theta_rtn, halo_temp_phi_rtn = rotate_temperature(
+                        current_epoch, halo_moment.alpha, halo_moment.beta
+                    )
                     halo_temp_theta_rtns[i] = halo_temp_theta_rtn
                     halo_temp_phi_rtns[i] = halo_temp_phi_rtn
-                    halo_temp_avg = (2 * halo_moment.t_perpendicular + halo_moment.t_parallel) / 3
+                    halo_temp_avg = (
+                        2 * halo_moment.t_perpendicular + halo_moment.t_parallel
+                    ) / 3
                     if 1e4 < halo_temp_avg < 1e8:
-                        halo_integrate_result = integrate(jbreak,
-                                                          len(corrected_energy_bins[i]) - 6,
-                                                          corrected_energy_bins[i],
-                                                          sin_theta, cos_theta,
-                                                          config['aperture_field_of_view_radians'],
-                                                          swe_l2_data.phase_space_density[i],
-                                                          instrument_phi[i],
-                                                          spacecraft_potential[i],
-                                                          [0, 0, 0, 0], [0, 0, 0, 0, 0, 0])
+                        halo_integrate_result = integrate(
+                            jbreak,
+                            len(corrected_energy_bins[i]) - 6,
+                            corrected_energy_bins[i],
+                            sin_theta,
+                            cos_theta,
+                            config["aperture_field_of_view_radians"],
+                            swe_l2_data.phase_space_density[i],
+                            instrument_phi[i],
+                            spacecraft_potential[i],
+                            [0, 0, 0, 0],
+                            [0, 0, 0, 0, 0, 0],
+                        )
                         if halo_integrate_result is not None:
-                            scale_halo_density_output: ScaleDensityOutput = scale_halo_density(
-                                halo_integrate_result.density,
-                                halo_integrate_result.velocity,
-                                halo_integrate_result.temperature, halo_moment,
-                                spacecraft_potential[i], halo_core[i], cos_theta,
-                                config['aperture_field_of_view_radians'],
-                                instrument_phi[i],
-                                halo_moment_fit_result.regress_result,
-                                halo_integrate_result.base_energy)
-
-                            halo_density_integrated[i] = scale_halo_density_output.density
-                            halo_velocity_integrated[i] = rotate_dps_vector_to_rtn(current_epoch,
-                                                                                   scale_halo_density_output.velocity)
-                            halo_temperature_tensor_integrated[i] = scale_halo_density_output.temperature
-
-                            magnitude, theta, phi = rotate_vector_to_rtn_spherical_coordinates(current_epoch,
-                                                                                               halo_integrate_result.heat_flux)
+                            scale_halo_density_output: ScaleDensityOutput = (
+                                scale_halo_density(
+                                    halo_integrate_result.density,
+                                    halo_integrate_result.velocity,
+                                    halo_integrate_result.temperature,
+                                    halo_moment,
+                                    spacecraft_potential[i],
+                                    halo_core[i],
+                                    cos_theta,
+                                    config["aperture_field_of_view_radians"],
+                                    instrument_phi[i],
+                                    halo_moment_fit_result.regress_result,
+                                    halo_integrate_result.base_energy,
+                                )
+                            )
+
+                            halo_density_integrated[i] = (
+                                scale_halo_density_output.density
+                            )
+                            halo_velocity_integrated[i] = rotate_dps_vector_to_rtn(
+                                current_epoch, scale_halo_density_output.velocity
+                            )
+                            halo_temperature_tensor_integrated[i] = (
+                                scale_halo_density_output.temperature
+                            )
+
+                            magnitude, theta, phi = (
+                                rotate_vector_to_rtn_spherical_coordinates(
+                                    current_epoch, halo_integrate_result.heat_flux
+                                )
+                            )
 
                             halo_heat_flux_magnitude[i] = magnitude
                             halo_heat_flux_theta[i] = theta
                             halo_heat_flux_phi[i] = phi
 
-                            halo_primary_eigen_vector, halo_temps = calculate_primary_eigenvector(
-                                scale_halo_density_output.temperature)
+                            halo_primary_eigen_vector, halo_temps = (
+                                calculate_primary_eigenvector(
+                                    scale_halo_density_output.temperature
+                                )
+                            )
 
                             halo_t_parallel_integrated[i] = halo_temps[0]
-                            halo_t_perpendicular_integrated[i] = [halo_temps[1], halo_temps[2]]
-
-                            magnitude, theta, phi = rotate_vector_to_rtn_spherical_coordinates(current_epoch,
-                                                                                               halo_primary_eigen_vector)
+                            halo_t_perpendicular_integrated[i] = [
+                                halo_temps[1],
+                                halo_temps[2],
+                            ]
+
+                            magnitude, theta, phi = (
+                                rotate_vector_to_rtn_spherical_coordinates(
+                                    current_epoch, halo_primary_eigen_vector
+                                )
+                            )
                             halo_temperature_theta_rtn_integrated[i] = theta
                             halo_temperature_phi_rtn_integrated[i] = phi
 
-                            halo_t_parallel_to_mag, halo_t_perpendicular_to_mag_average, halo_t_perpendicular_to_mag_ratio = rotate_temperature_tensor_to_mag(
-                                scale_halo_density_output.temperature, rebinned_mag_data[i])
+                            (
+                                halo_t_parallel_to_mag,
+                                halo_t_perpendicular_to_mag_average,
+                                halo_t_perpendicular_to_mag_ratio,
+                            ) = rotate_temperature_tensor_to_mag(
+                                scale_halo_density_output.temperature,
+                                rebinned_mag_data[i],
+                            )
 
                             halo_temperature_parallel_to_mag[i] = halo_t_parallel_to_mag
-                            halo_temperature_perpendicular_to_mag[i] = [halo_t_perpendicular_to_mag_average,
-                                                                        halo_t_perpendicular_to_mag_ratio]
+                            halo_temperature_perpendicular_to_mag[i] = [
+                                halo_t_perpendicular_to_mag_average,
+                                halo_t_perpendicular_to_mag_ratio,
+                            ]
                         else:
                             logger.info(f"halo integrate failed at index {i}")
                     else:
-                        logger.info(f"halo temp {halo_temp_avg} out of range at index {i}")
+                        logger.info(
+                            f"halo temp {halo_temp_avg} out of range at index {i}"
+                        )
                 else:
                     logger.info(f"bad halo moment fit result at index {i}")
 
             except Exception:
                 logger.info(
                     f"Failed to process moments at Epoch index: {i}. Outputting fill values and continuing to process. Traceback:",
-                    exc_info=True)
+                    exc_info=True,
+                )
                 continue
 
         return SweL3MomentData(
             core_fit_num_points=core_fit_num_points,
             core_chisq=core_fit_chi_squareds,
             halo_chisq=halo_fit_chi_squareds,
-            core_density_fit=np.array([core_moment.density for core_moment in core_moments]),
-            halo_density_fit=np.array([halo_moment.density for halo_moment in halo_moments]),
-            core_t_parallel_fit=np.array([core_moment.t_parallel for core_moment in core_moments]),
-            halo_t_parallel_fit=np.array([halo_moment.t_parallel for halo_moment in halo_moments]),
-            core_t_perpendicular_fit=np.array([core_moment.t_perpendicular for core_moment in core_moments]),
-            halo_t_perpendicular_fit=np.array([halo_moment.t_perpendicular for halo_moment in halo_moments]),
+            core_density_fit=np.array(
+                [core_moment.density for core_moment in core_moments]
+            ),
+            halo_density_fit=np.array(
+                [halo_moment.density for halo_moment in halo_moments]
+            ),
+            core_t_parallel_fit=np.array(
+                [core_moment.t_parallel for core_moment in core_moments]
+            ),
+            halo_t_parallel_fit=np.array(
+                [halo_moment.t_parallel for halo_moment in halo_moments]
+            ),
+            core_t_perpendicular_fit=np.array(
+                [core_moment.t_perpendicular for core_moment in core_moments]
+            ),
+            halo_t_perpendicular_fit=np.array(
+                [halo_moment.t_perpendicular for halo_moment in halo_moments]
+            ),
             core_temperature_phi_rtn_fit=np.rad2deg(core_temp_phi_rtns),
             halo_temperature_phi_rtn_fit=np.rad2deg(halo_temp_phi_rtns),
             core_temperature_theta_rtn_fit=np.rad2deg(core_temp_theta_rtns),
@@ -540,12 +784,24 @@ def calculate_moment_products(self, swe_l2_data: SweL2Data, swe_l1b_data: SweL1b
             halo_t_perpendicular_integrated=halo_t_perpendicular_integrated,
             total_t_parallel_integrated=total_t_parallel_integrated,
             total_t_perpendicular_integrated=total_t_perpendicular_integrated,
-            core_temperature_theta_rtn_integrated=np.rad2deg(core_temperature_theta_rtn_integrated),
-            core_temperature_phi_rtn_integrated=np.rad2deg(core_temperature_phi_rtn_integrated),
-            halo_temperature_theta_rtn_integrated=np.rad2deg(halo_temperature_theta_rtn_integrated),
-            halo_temperature_phi_rtn_integrated=np.rad2deg(halo_temperature_phi_rtn_integrated),
-            total_temperature_theta_rtn_integrated=np.rad2deg(total_temperature_theta_rtn_integrated),
-            total_temperature_phi_rtn_integrated=np.rad2deg(total_temperature_phi_rtn_integrated),
+            core_temperature_theta_rtn_integrated=np.rad2deg(
+                core_temperature_theta_rtn_integrated
+            ),
+            core_temperature_phi_rtn_integrated=np.rad2deg(
+                core_temperature_phi_rtn_integrated
+            ),
+            halo_temperature_theta_rtn_integrated=np.rad2deg(
+                halo_temperature_theta_rtn_integrated
+            ),
+            halo_temperature_phi_rtn_integrated=np.rad2deg(
+                halo_temperature_phi_rtn_integrated
+            ),
+            total_temperature_theta_rtn_integrated=np.rad2deg(
+                total_temperature_theta_rtn_integrated
+            ),
+            total_temperature_phi_rtn_integrated=np.rad2deg(
+                total_temperature_phi_rtn_integrated
+            ),
             core_temperature_parallel_to_mag=core_temperature_parallel_to_mag,
             core_temperature_perpendicular_to_mag=core_temperature_perpendicular_to_mag,
             halo_temperature_parallel_to_mag=halo_temperature_parallel_to_mag,
@@ -557,35 +813,59 @@ def calculate_moment_products(self, swe_l2_data: SweL2Data, swe_l1b_data: SweL1b
             total_temperature_tensor_integrated=total_temperature_tensor_integrated,
         )
 
-    def calculate_pitch_angle_products(self, dependencies: SweL3Dependencies, corrected_energy_bins: np.ndarray):
+    def calculate_pitch_angle_products(
+        self, dependencies: SweL3Dependencies, corrected_energy_bins: np.ndarray
+    ):
         swe_l2_data = dependencies.swe_l2_data
         swe_epoch = swe_l2_data.epoch
         config = dependencies.configuration
-        mag_max_distance = np.timedelta64(int(config['max_mag_offset_in_minutes'] * 60e9), 'ns')
+        mag_max_distance = np.timedelta64(
+            int(config["max_mag_offset_in_minutes"] * 60e9), "ns"
+        )
 
-        rebinned_mag_data, indices = find_closest_neighbor(from_epoch=dependencies.mag_data.epoch,
-                                                           from_data=dependencies.mag_data.mag_data,
-                                                           to_epoch=swe_l2_data.acquisition_time,
-                                                           maximum_distance=mag_max_distance,
-                                                           )
+        rebinned_mag_data, indices = find_closest_neighbor(
+            from_epoch=dependencies.mag_data.epoch,
+            from_data=dependencies.mag_data.mag_data,
+            to_epoch=swe_l2_data.acquisition_time,
+            maximum_distance=mag_max_distance,
+        )
 
         swapi_l3a_proton_data = dependencies.swapi_l3a_proton_data
         swapi_epoch = swapi_l3a_proton_data.epoch
-        solar_wind_vectors = calculate_solar_wind_velocity_vector(swapi_l3a_proton_data.proton_sw_speed,
-                                                                  swapi_l3a_proton_data.proton_sw_clock_angle,
-                                                                  swapi_l3a_proton_data.proton_sw_deflection_angle)
-        swapi_max_distance = np.timedelta64(int(config['max_swapi_offset_in_minutes'] * 60e9), 'ns')
-        rebinned_solar_wind_vectors, swapi_indices = find_closest_neighbor(from_epoch=swapi_epoch,
-                                                                           from_data=solar_wind_vectors,
-                                                                           to_epoch=swe_epoch,
-                                                                           maximum_distance=swapi_max_distance)
-
-        closest_flags = swapi_l3a_proton_data.swp_flags[swapi_indices]
+        solar_wind_vectors = rotate_rtn_vectors_to_dps(
+            swapi_epoch,
+            swapi_l3a_proton_data.proton_sw_velocity_rtn,
+        )
+        proton_sw_speed = swapi_l3a_proton_data.proton_sw_speed
+        fallback_to_speed = np.any(np.isnan(solar_wind_vectors), axis=1) & np.isfinite(
+            proton_sw_speed
+        )
+        speed_fallback_vectors = np.zeros_like(solar_wind_vectors)
+        speed_fallback_vectors[:, 2] = -proton_sw_speed
+        solar_wind_vectors = np.where(
+            fallback_to_speed[:, np.newaxis],
+            speed_fallback_vectors,
+            solar_wind_vectors,
+        )
+        swapi_max_distance = np.timedelta64(
+            int(config["max_swapi_offset_in_minutes"] * 60e9), "ns"
+        )
+        rebinned_solar_wind_vectors, swapi_indices = find_closest_neighbor(
+            from_epoch=swapi_epoch,
+            from_data=solar_wind_vectors,
+            to_epoch=swe_epoch,
+            maximum_distance=swapi_max_distance,
+        )
+
+        kept_after_nan_filter = ~np.any(np.isnan(solar_wind_vectors), axis=1)
+        fallback_at_swe = fallback_to_speed[kept_after_nan_filter][swapi_indices]
+        within_window = ~np.any(np.isnan(rebinned_solar_wind_vectors), axis=1)
         swe_flags = np.full(len(swe_epoch), SweL3Flags.NONE, dtype=np.uint16)
-        estimated_angles = (closest_flags & SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED) > 0
-        swe_flags[estimated_angles] = SweL3Flags.FALLBACK_SWAPI_SPEED
+        swe_flags[fallback_at_swe & within_window] = SweL3Flags.FALLBACK_SWAPI_SPEED
 
-        counts = dependencies.swe_l1b_data.count_rates * (swe_l2_data.acquisition_duration[..., np.newaxis] / 1e6)
+        counts = dependencies.swe_l1b_data.count_rates * (
+            swe_l2_data.acquisition_duration[..., np.newaxis] / 1e6
+        )
 
         phase_space_density_by_pitch_angle = []
         phase_space_density_by_pitch_angle_and_gyrophase = []
@@ -600,56 +880,102 @@ def calculate_pitch_angle_products(self, dependencies: SweL3Dependencies, correc
         for i in range(len(swe_epoch)):
             missing_mag_data = np.any(np.isnan(rebinned_mag_data[i]))
             if missing_mag_data:
-                num_energy_bins = len(config['energy_bins'])
-                num_pitch_angle_bins = len(config['pitch_angle_bins'])
-                num_gyrophase_bins = len(config['gyrophase_bins'])
-                phase_space_density_by_pitch_angle.append(np.full((num_energy_bins, num_pitch_angle_bins), np.nan))
+                num_energy_bins = len(config["energy_bins"])
+                num_pitch_angle_bins = len(config["pitch_angle_bins"])
+                num_gyrophase_bins = len(config["gyrophase_bins"])
+                phase_space_density_by_pitch_angle.append(
+                    np.full((num_energy_bins, num_pitch_angle_bins), np.nan)
+                )
                 phase_space_density_by_pitch_angle_and_gyrophase.append(
-                    np.full((num_energy_bins, num_pitch_angle_bins, num_gyrophase_bins), np.nan))
+                    np.full(
+                        (num_energy_bins, num_pitch_angle_bins, num_gyrophase_bins),
+                        np.nan,
+                    )
+                )
                 phase_space_density_1d.append(np.full(num_energy_bins, np.nan))
                 phase_space_density_inward.append(np.full(num_energy_bins, np.nan))
                 phase_space_density_outward.append(np.full(num_energy_bins, np.nan))
 
                 rebinned_intensity_by_pa_and_gyro.append(
-                    np.full((swe_l2_data.flux.shape[1], num_pitch_angle_bins, num_gyrophase_bins), np.nan))
-                rebinned_intensity_by_pa.append(np.full((swe_l2_data.flux.shape[1], num_pitch_angle_bins), np.nan))
+                    np.full(
+                        (
+                            swe_l2_data.flux.shape[1],
+                            num_pitch_angle_bins,
+                            num_gyrophase_bins,
+                        ),
+                        np.nan,
+                    )
+                )
+                rebinned_intensity_by_pa.append(
+                    np.full((swe_l2_data.flux.shape[1], num_pitch_angle_bins), np.nan)
+                )
                 uncertainties_by_pa_and_gyro.append(
-                    np.full((swe_l2_data.flux.shape[1], num_pitch_angle_bins, num_gyrophase_bins), np.nan))
-                uncertainties_by_pa.append(np.full((swe_l2_data.flux.shape[1], num_pitch_angle_bins), np.nan))
+                    np.full(
+                        (
+                            swe_l2_data.flux.shape[1],
+                            num_pitch_angle_bins,
+                            num_gyrophase_bins,
+                        ),
+                        np.nan,
+                    )
+                )
+                uncertainties_by_pa.append(
+                    np.full((swe_l2_data.flux.shape[1], num_pitch_angle_bins), np.nan)
+                )
             else:
-                dsp_velocities = calculate_velocity_in_dsp_frame_km_s(corrected_energy_bins[i], swe_l2_data.inst_el,
-                                                                      swe_l2_data.inst_az_spin_sector[i])
-                rebinned_psd, rebinned_psd_by_pa_and_gyro = correct_and_rebin(swe_l2_data.phase_space_density[i],
-                                                                              rebinned_solar_wind_vectors[i],
-                                                                              dsp_velocities,
-                                                                              rebinned_mag_data[i],
-                                                                              config)
+                dsp_velocities = calculate_velocity_in_dsp_frame_km_s(
+                    corrected_energy_bins[i],
+                    swe_l2_data.inst_el,
+                    swe_l2_data.inst_az_spin_sector[i],
+                )
+                rebinned_psd, rebinned_psd_by_pa_and_gyro = correct_and_rebin(
+                    swe_l2_data.phase_space_density[i],
+                    rebinned_solar_wind_vectors[i],
+                    dsp_velocities,
+                    rebinned_mag_data[i],
+                    config,
+                )
                 phase_space_density_by_pitch_angle.append(rebinned_psd)
-                phase_space_density_by_pitch_angle_and_gyrophase.append(rebinned_psd_by_pa_and_gyro)
-                phase_space_density_1d.append(integrate_distribution_to_get_1d_spectrum(rebinned_psd, config))
-                inbound, outbound = integrate_distribution_to_get_inbound_and_outbound_1d_spectrum(rebinned_psd,
-                                                                                                   config)
+                phase_space_density_by_pitch_angle_and_gyrophase.append(
+                    rebinned_psd_by_pa_and_gyro
+                )
+                phase_space_density_1d.append(
+                    integrate_distribution_to_get_1d_spectrum(rebinned_psd, config)
+                )
+                inbound, outbound = (
+                    integrate_distribution_to_get_inbound_and_outbound_1d_spectrum(
+                        rebinned_psd, config
+                    )
+                )
                 phase_space_density_inward.append(inbound)
                 phase_space_density_outward.append(outbound)
 
-                intensity_by_pa_and_gyro, intensity_by_pa, uncertainty_by_pa_and_gyro, uncertainty_by_pa = swe_rebin_intensity_by_pitch_angle_and_gyrophase(
+                (
+                    intensity_by_pa_and_gyro,
+                    intensity_by_pa,
+                    uncertainty_by_pa_and_gyro,
+                    uncertainty_by_pa,
+                ) = swe_rebin_intensity_by_pitch_angle_and_gyrophase(
                     swe_l2_data.flux[i],
                     counts[i],
                     dsp_velocities,
                     rebinned_mag_data[i],
-                    config)
+                    config,
+                )
                 rebinned_intensity_by_pa_and_gyro.append(intensity_by_pa_and_gyro)
                 rebinned_intensity_by_pa.append(intensity_by_pa)
                 uncertainties_by_pa_and_gyro.append(uncertainty_by_pa_and_gyro)
                 uncertainties_by_pa.append(uncertainty_by_pa)
 
-        return phase_space_density_by_pitch_angle, \
-            phase_space_density_by_pitch_angle_and_gyrophase, \
-            phase_space_density_1d, \
-            phase_space_density_inward, \
-            phase_space_density_outward, \
-            np.array(rebinned_intensity_by_pa_and_gyro), \
-            np.array(rebinned_intensity_by_pa), \
-            np.array(uncertainties_by_pa_and_gyro), \
-            np.array(uncertainties_by_pa), \
-            swe_flags
+        return (
+            phase_space_density_by_pitch_angle,
+            phase_space_density_by_pitch_angle_and_gyrophase,
+            phase_space_density_1d,
+            phase_space_density_inward,
+            phase_space_density_outward,
+            np.array(rebinned_intensity_by_pa_and_gyro),
+            np.array(rebinned_intensity_by_pa),
+            np.array(uncertainties_by_pa_and_gyro),
+            np.array(uncertainties_by_pa),
+            swe_flags,
+        )
diff --git a/imap_l3_processing/utils.py b/imap_l3_processing/utils.py
index d0a9c1d11..7d7a17e14 100644
--- a/imap_l3_processing/utils.py
+++ b/imap_l3_processing/utils.py
@@ -10,7 +10,7 @@
 import imap_data_access
 import requests
 import spiceypy
-from imap_data_access import ScienceFilePath
+from imap_data_access import ScienceFilePath, download
 from imap_data_access.processing_input import ProcessingInputCollection
 from requests import RequestException
 from spacepy.pycdf import CDF
@@ -223,6 +223,24 @@ def read_mag_data(cdf_path: Union[str, Path]) -> MagData:
             mag_data=read_numeric_variable(cdf["b_dsrf"])[:, :3])
 
 
+def select_mag_path(
+    dependencies: ProcessingInputCollection,
+    descriptor: str,
+) -> tuple[Optional[Path], Optional[str]]:
+    science_files = dependencies.processing_input
+    for level in ("l2", "l1d"):
+        match = next(
+            (d.imap_file_paths[0] for d in science_files
+             if d.source == "mag"
+             and d.descriptor == descriptor
+             and d.data_type == level),
+            None,
+        )
+        if match is not None:
+            return download(match.construct_path()), level
+    return None, None
+
+
 L1CPointingSet = TypeVar("L1CPointingSet", bound=Union[InputRectangularPointingSet, UltraL1CPSet])
 GlowsL3eData = TypeVar("GlowsL3eData", bound=Union[GlowsL3eRectangularMapInputData, UltraGlowsL3eData])
 
diff --git a/instrument_team_data/swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv b/instrument_team_data/swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv
new file mode 100644
index 000000000..9facc25c9
--- /dev/null
+++ b/instrument_team_data/swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv
@@ -0,0 +1,1802 @@
+abs_azimuth,transmission
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diff --git a/instrument_team_data/swapi/imap_swapi_central-effective-area_20260425_v001.csv b/instrument_team_data/swapi/imap_swapi_central-effective-area_20260425_v001.csv
new file mode 100644
index 000000000..dd499e13a
--- /dev/null
+++ b/instrument_team_data/swapi/imap_swapi_central-effective-area_20260425_v001.csv
@@ -0,0 +1,63 @@
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diff --git a/instrument_team_data/swapi/imap_swapi_passband-fit-coefficients_20260425_v001.csv b/instrument_team_data/swapi/imap_swapi_passband-fit-coefficients_20260425_v001.csv
new file mode 100644
index 000000000..ed8292a39
--- /dev/null
+++ b/instrument_team_data/swapi/imap_swapi_passband-fit-coefficients_20260425_v001.csv
@@ -0,0 +1,1147 @@
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diff --git a/pyproject.toml b/pyproject.toml
index 71ea78511..4588d10eb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,7 +27,8 @@ dependencies = [
     "xarray==2025.3.1",
     "imap-processing>=1.0.29",
     "hatchling",
-    "uv-dynamic-versioning"
+    "uv-dynamic-versioning",
+    "numba>=0.65.1",
 ]
 
 [dependency-groups]
diff --git a/run_local.py b/run_local.py
index c3f893252..ffcff7c4f 100644
--- a/run_local.py
+++ b/run_local.py
@@ -58,13 +58,7 @@
 from imap_l3_processing.maps.map_models import RectangularSpectralIndexDataProduct, RectangularIntensityDataProduct, \
     RectangularIntensityMapData
 from imap_l3_processing.models import InputMetadata
-from imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_temperature_and_density import \
-    AlphaTemperatureDensityCalibrationTable
 from imap_l3_processing.swapi.l3a.science.calculate_pickup_ion import DensityOfNeutralHeliumLookupTable
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_clock_and_deflection_angles import \
-    ClockAngleCalibrationTable
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_temperature_and_density import \
-    ProtonTemperatureAndDensityCalibrationTable
 from imap_l3_processing.swapi.l3a.swapi_l3a_dependencies import SwapiL3ADependencies
 from imap_l3_processing.swapi.l3a.utils import read_l2_swapi_data
 from imap_l3_processing.swapi.l3b.science.efficiency_calibration_table import EfficiencyCalibrationTable
@@ -243,8 +237,7 @@ def create_swapi_l3b_cdf(geometric_calibration_file, efficiency_calibration_file
 
 
 @patch("imap_l3_processing.swapi.l3a.science.calculate_pickup_ion.spiceypy")
-def create_swapi_l3a_cdf(proton_temperature_density_calibration_file, alpha_temperature_density_calibration_file,
-                         clock_angle_and_flow_deflection_calibration_file, geometric_factor_calibration_file,
+def create_swapi_l3a_cdf(geometric_factor_calibration_file,
                          instrument_response_calibration_file, density_of_neutral_helium_calibration_file,
                          imap_swapi_efficiency_lut_file, cdf_file, mock_spice):
     ephemeris_time_for_epoch = int(100000 * 1e9)
@@ -268,12 +261,6 @@ def mock_sxform(from_frame, to_frame, et):
 
     mock_spice.sxform.side_effect = mock_sxform
 
-    proton_temperature_density_calibration_table = ProtonTemperatureAndDensityCalibrationTable.from_file(
-        proton_temperature_density_calibration_file)
-    alpha_temperature_density_calibration_table = AlphaTemperatureDensityCalibrationTable.from_file(
-        alpha_temperature_density_calibration_file)
-    clock_angle_and_flow_deflection_calibration_table = ClockAngleCalibrationTable.from_file(
-        clock_angle_and_flow_deflection_calibration_file)
     efficiency_calibration_table = EfficiencyCalibrationTable(imap_swapi_efficiency_lut_file)
     geometric_factor_calibration_table = GeometricFactorCalibrationTable.from_file(geometric_factor_calibration_file)
     instrument_response_calibration_table = InstrumentResponseLookupTableCollection.from_file(
@@ -282,13 +269,16 @@ def mock_sxform(from_frame, to_frame, et):
         density_of_neutral_helium_calibration_file)
     swapi_cdf_data = CDF(cdf_file)
     swapi_data = read_l2_swapi_data(swapi_cdf_data)
-    swapi_l3_dependencies = SwapiL3ADependencies(swapi_data, proton_temperature_density_calibration_table,
-                                                 alpha_temperature_density_calibration_table,
-                                                 clock_angle_and_flow_deflection_calibration_table,
-                                                 efficiency_calibration_table,
-                                                 geometric_factor_calibration_table,
-                                                 instrument_response_calibration_table,
-                                                 density_of_neutral_helium_calibration_table)
+    swapi_l3_dependencies = SwapiL3ADependencies(
+        data=swapi_data,
+        efficiency_calibration_table=efficiency_calibration_table,
+        geometric_factor_calibration_table=geometric_factor_calibration_table,
+        instrument_response_calibration_table=instrument_response_calibration_table,
+        density_of_neutral_helium_calibration_table=density_of_neutral_helium_calibration_table,
+        hydrogen_inflow_vector=Mock(),
+        helium_inflow_vector=Mock(),
+        swapi_response=Mock(),
+    )
 
     input_metadata = InputMetadata(
         instrument='swapi',
@@ -299,7 +289,7 @@ def mock_sxform(from_frame, to_frame, et):
     processor = SwapiProcessor(Mock(), input_metadata)
 
     l3a_proton_sw = processor.process_l3a_proton(swapi_data, swapi_l3_dependencies)
-    l3a_alpha_sw = processor.process_l3a_alpha_solar_wind(swapi_data, swapi_l3_dependencies)
+    l3a_alpha_sw = processor.process_l3a_alpha(swapi_data, swapi_l3_dependencies)
     l3a_pui_he = processor.process_l3a_pui(swapi_data, swapi_l3_dependencies)
     proton_cdf_path = save_data(l3a_proton_sw, delete_if_present=True)
     alpha_cdf_path = save_data(l3a_alpha_sw, delete_if_present=True)
@@ -1030,9 +1020,6 @@ def create_codice_hi_l3b_pitch_angles_cdf():
     if "swapi" in sys.argv:
         if "l3a" in sys.argv:
             paths = create_swapi_l3a_cdf(
-                str(get_test_data_path("swapi/imap_swapi_proton-density-temperature-lut_20240905_v001.dat")),
-                str(get_test_data_path("swapi/imap_swapi_alpha-density-temperature-lut_20240920_v000.dat")),
-                str(get_test_data_path("swapi/imap_swapi_clock-angle-and-flow-deflection-lut_20240918_v001.dat")),
                 str(get_test_data_path("swapi/imap_swapi_energy-gf-pui-lut_20100101_v001.csv")),
                 str(get_test_data_path("swapi/imap_swapi_instrument-response-lut_20241023_v000.zip")),
                 str(get_test_data_path(
diff --git a/run_local_with_upload_and_download.py b/run_local_with_upload_and_download.py
index 0147af89f..92c957bee 100644
--- a/run_local_with_upload_and_download.py
+++ b/run_local_with_upload_and_download.py
@@ -127,13 +127,14 @@
                             '--descriptor', 'proton-sw', '--start-date', '20250606', '--version', 'v000',
                             '--dependency',
                             '[{"type":"science","files":["imap_swapi_l2_sci_20251208_v006.cdf"]},'
-                            '{"type":"ancillary","files":["imap_swapi_proton-density-temperature-lut_20240905_v000.dat"]},'
                             '{"type":"ancillary","files":["imap_swapi_alpha-density-temperature-lut_20240920_v000.dat"]},'
-                            '{"type":"ancillary","files":["imap_swapi_clock-angle-and-flow-deflection-lut_20240918_v000.dat"]},'
                             '{"type":"ancillary","files":["imap_swapi_efficiency-lut_20241020_v000.dat"]},'
                             '{"type":"ancillary","files":["imap_swapi_energy-gf-pui-lut_20100101_v001.csv"]},'
                             '{"type":"ancillary","files":["imap_swapi_instrument-response-lut_20241023_v000.zip"]},'
                             '{"type":"ancillary","files":["imap_swapi_density-of-neutral-helium-lut_20241023_v000.dat"]},'
+                            '{"type":"ancillary","files":["imap_swapi_azimuthal-transmission_20260425_v001.csv"]},'
+                            '{"type":"ancillary","files":["imap_swapi_central-effective-area_20260425_v001.csv"]},'
+                            '{"type":"ancillary","files":["imap_swapi_passband-fit-coefficients_20260425_v001.csv"]},'
                             '{"type": "spice", "files": ["imap_sclk_0000.tsc"]},' +
                             '{"type": "spice", "files": ["naif0012.tls"]},' +
                             '{"type": "spice", "files": ["imap_science_108.tf"]},' +
@@ -146,9 +147,7 @@
                             '--descriptor', 'alpha-sw', '--start-date', '20250606', '--version', 'v000',
                             '--dependency',
                             '[{"type":"science","files":["imap_swapi_l2_sci_20250606_v001.cdf"]},'
-                            '{"type":"ancillary","files":["imap_swapi_proton-density-temperature-lut_20240905_v000.dat"]},'
                             '{"type":"ancillary","files":["imap_swapi_alpha-density-temperature-lut_20240920_v000.dat"]},'
-                            '{"type":"ancillary","files":["imap_swapi_clock-angle-and-flow-deflection-lut_20240918_v000.dat"]},'
                             '{"type":"ancillary","files":["imap_swapi_efficiency-lut_20241020_v000.dat"]},'
                             '{"type":"ancillary","files":["imap_swapi_energy-gf-pui-lut_20100101_v001.csv"]},'
                             '{"type":"ancillary","files":["imap_swapi_instrument-response-lut_20241023_v000.zip"]},'
@@ -159,9 +158,7 @@
                             '--descriptor', 'pui-he', '--start-date', '20250606', '--version', 'v000',
                             '--dependency',
                             '[{"type":"science","files":["imap_swapi_l2_sci_20250606_v001.cdf"]},'
-                            '{"type":"ancillary","files":["imap_swapi_proton-density-temperature-lut_20240905_v000.dat"]},'
                             '{"type":"ancillary","files":["imap_swapi_alpha-density-temperature-lut_20240920_v000.dat"]},'
-                            '{"type":"ancillary","files":["imap_swapi_clock-angle-and-flow-deflection-lut_20240918_v000.dat"]},'
                             '{"type":"ancillary","files":["imap_swapi_efficiency-lut_20241020_v000.dat"]},'
                             '{"type":"ancillary","files":["imap_swapi_energy-gf-pui-lut_20100101_v001.csv"]},'
                             '{"type":"ancillary","files":["imap_swapi_instrument-response-lut_20241023_v000.zip"]},'
diff --git a/scripts/swapi/__init__.py b/scripts/swapi/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/scripts/swapi/fake_proton_sw_temperature_density_lut_generator.py b/scripts/swapi/fake_proton_sw_temperature_density_lut_generator.py
deleted file mode 100644
index 2dad80fa5..000000000
--- a/scripts/swapi/fake_proton_sw_temperature_density_lut_generator.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import itertools
-
-import numpy as np
-
-u_sw_values = np.arange(350, 875, 500)
-deflection_values = np.arange(0, 5.25, 5)
-clock_angles = np.arange(0, 365, 360)
-densities = np.arange(1, 6.5, 0.5)
-temps = np.arange(1e4, 1.6e5, 1e4)
-
-with open("imap_swapi_proton-density-temperature-lut_20240905_v000.dat", 'w') as f:
-    f.write("#\n")
-    f.write("# Created on: 2024-08-23T20:04:16.44396543502487Z\n")
-    f.write("#\n")
-    f.write("# Example LUT for SW Density and Temperature:\n")
-    f.write("#\n")
-    f.write("#   u_sw     defln_angle     clock_angle     E[n_i] density (cm^-3)     E[T_i] temperature (K)\n")
-
-    for u_sw, defln_angle, clock_angle, density, temp in itertools.product(u_sw_values, deflection_values, clock_angles,
-                                                                           densities, temps):
-        f.write(
-            f"{u_sw}  {defln_angle}  {clock_angle}  {density}  {density * 1.021:.4f}  {temp:.4e}  {temp / 1.025:0.4e}\n")
diff --git a/scripts/swapi/generate_reference_integrals.py b/scripts/swapi/generate_reference_integrals.py
new file mode 100644
index 000000000..135708558
--- /dev/null
+++ b/scripts/swapi/generate_reference_integrals.py
@@ -0,0 +1,144 @@
+#!/usr/bin/env python3
+"""
+Generate reference ground-truth integrals for SWAPI proton solar wind.
+
+Computes 10000 reference integrals using fixed limits covering the full passband
+at high resolution. These are used as ground truth in regression tests and the
+scatter benchmark.
+
+Fixed integration limits:
+  elevation:  -15 to 15 deg at 0.05 deg (601 pts)
+  azimuth SG: -20 to 20 deg at 0.05 deg (801 pts)
+  azimuth OA: 0.05 deg in transition |az| ∈ [20, 30], 0.5 deg in bulk to ±150 (441 pts/side)
+  speed: 200 samples from 0.9 to 1.1 × central_speed
+
+Solar wind parameter ranges (10000 samples, seed=42):
+  bulk_speed:      200–2000 km/s   (uniform)
+  temperature:     1–100 eV        (log-uniform)
+  bulk_azimuth:    -20 to 20 deg   (uniform)
+  bulk_elevation:  -20 to 20 deg   (uniform)
+  density:         1–100 cm⁻³      (uniform)
+
+Output: tests/test_data/swapi/reference_integrals.csv
+Usage:  python scripts/swapi/generate_reference_integrals.py
+"""
+
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
+
+import numpy as np
+import pandas as pd
+
+from imap_l3_processing.constants import (
+    EV_TO_KELVIN,
+    METERS_PER_KILOMETER,
+    PROTON_CHARGE_COULOMBS,
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
+from scripts.swapi.reference_integral import reference_integrals_batch
+
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+_INSTRUMENT_DATA = _REPO_ROOT / "instrument_team_data" / "swapi"
+_OUTPUT_PATH = (
+    _REPO_ROOT / "tests" / "test_data" / "swapi" / "reference_integrals.csv"
+)
+
+_N_SAMPLES = 10000
+_RNG_SEED = 42
+
+_SPEED_RANGE = (200.0, 2000.0)
+_TEMP_RANGE = (1.0, 100.0)
+_AZ_RANGE = (-20.0, 20.0)
+_EL_RANGE = (-20.0, 20.0)
+_DENSITY_RANGE = (1.0, 100.0)
+
+
+def _peak_voltage(bulk_speed_km_s: float) -> float:
+    return (
+        PROTON_MASS_KG
+        * (bulk_speed_km_s * METERS_PER_KILOMETER) ** 2
+        / (2 * SWAPI_K_FACTOR * PROTON_CHARGE_COULOMBS)
+    )
+
+
+def _bulk_velocity_rtn(speed: float, az_deg: float, el_deg: float) -> np.ndarray:
+    """Inverse of `bulk_angles_in_instrument_frame` with R = identity (RTN ≡ SWAPI)."""
+    az = np.radians(az_deg)
+    el = np.radians(el_deg)
+    horizontal = speed * np.cos(el)
+    return np.array(
+        [
+            -horizontal * np.sin(az),
+            -horizontal * np.cos(az),
+            -speed * np.sin(el),
+        ]
+    )
+
+
+def main():
+    print("Loading calibration data...")
+    swapi_response = SwapiResponse.from_files(
+        _INSTRUMENT_DATA / "imap_swapi_azimuthal-transmission_20260425_v001.csv",
+        _INSTRUMENT_DATA / "imap_swapi_central-effective-area_20260425_v001.csv",
+        _INSTRUMENT_DATA / "imap_swapi_passband-fit-coefficients_20260425_v001.csv",
+    )
+
+    rng = np.random.default_rng(_RNG_SEED)
+    bulk_speeds = np.round(rng.uniform(*_SPEED_RANGE, _N_SAMPLES), 1)
+    temperatures_ev = np.round(
+        np.exp(
+            rng.uniform(np.log(_TEMP_RANGE[0]), np.log(_TEMP_RANGE[1]), _N_SAMPLES)
+        ),
+        1,
+    )
+    bulk_azimuths = np.round(rng.uniform(*_AZ_RANGE, _N_SAMPLES), 1)
+    bulk_elevations = np.round(rng.uniform(*_EL_RANGE, _N_SAMPLES), 1)
+    densities = np.round(rng.uniform(*_DENSITY_RANGE, _N_SAMPLES), 1)
+
+    print(f"Building grids and SWParams for {_N_SAMPLES} samples...")
+    sws = [
+        SolarWindParams(
+            density=float(densities[i]),
+            bulk_velocity_rtn=_bulk_velocity_rtn(
+                float(bulk_speeds[i]),
+                float(bulk_azimuths[i]),
+                float(bulk_elevations[i]),
+            ),
+            temperature=float(temperatures_ev[i]) * EV_TO_KELVIN,
+            mass=PROTON_MASS_KG,
+        )
+        for i in range(_N_SAMPLES)
+    ]
+    rotation_matrices = np.broadcast_to(np.eye(3), (_N_SAMPLES, 3, 3))
+    voltages = [_peak_voltage(float(v)) for v in bulk_speeds]
+    swapi_response.warm_cache(voltages)
+    response_grids = [
+        swapi_response.get_response_grid(v, PROTON_MASS_PER_CHARGE_M_P_PER_E)
+        for v in voltages
+    ]
+
+    print(f"Computing {_N_SAMPLES} reference integrals (JIT-parallel, fixed limits)...")
+    integrals = reference_integrals_batch(response_grids, sws, rotation_matrices)
+
+    df = pd.DataFrame(
+        {
+            "bulk_speed": bulk_speeds,
+            "temperature_ev": temperatures_ev,
+            "bulk_azimuth": bulk_azimuths,
+            "bulk_elevation": bulk_elevations,
+            "density": densities,
+            "integral": np.round(integrals, 2),
+        }
+    )
+    df.to_csv(_OUTPUT_PATH, index=False)
+    print(f"Saved {_OUTPUT_PATH}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/swapi/imap_swapi_l3a_proton-sw_dependency_template.json b/scripts/swapi/imap_swapi_l3a_proton-sw_dependency_template.json
index 971cf2443..023b352d3 100644
--- a/scripts/swapi/imap_swapi_l3a_proton-sw_dependency_template.json
+++ b/scripts/swapi/imap_swapi_l3a_proton-sw_dependency_template.json
@@ -2,61 +2,67 @@
   {
     "type": "ancillary",
     "files": [
-      "imap_swapi_clock-angle-and-flow-deflection-lut_20240918_v003.dat"
+      "imap_swapi_efficiency-lut_20241020_v001.dat"
     ]
   },
   {
     "type": "ancillary",
     "files": [
-      "imap_swapi_proton-density-temperature-lut_20240905_v002.dat"
+      "imap_swapi_alpha-density-temperature-lut_20250125_v001.dat"
     ]
   },
   {
     "type": "ancillary",
     "files": [
-      "imap_swapi_efficiency-lut_20241020_v001.dat"
+      "imap_swapi_energy-gf-pui-lut_20100101_v003.csv"
     ]
   },
   {
     "type": "ancillary",
     "files": [
-      "imap_swapi_alpha-density-temperature-lut_20250125_v001.dat"
+      "imap_swapi_energy-gf-sw-lut_20100101_v003.csv"
     ]
   },
   {
     "type": "ancillary",
     "files": [
-      "imap_swapi_energy-gf-pui-lut_20100101_v003.csv"
+      "imap_swapi_instrument-response-lut_20241023_v001.zip"
     ]
   },
   {
     "type": "ancillary",
     "files": [
-      "imap_swapi_energy-gf-sw-lut_20100101_v003.csv"
+      "imap_swapi_density-of-neutral-helium-lut_20241023_v002.dat"
     ]
   },
   {
     "type": "ancillary",
     "files": [
-      "imap_swapi_instrument-response-lut_20241023_v001.zip"
+      "imap_swapi_helium-inflow-vector_20100101_v001.dat"
     ]
   },
   {
     "type": "ancillary",
     "files": [
-      "imap_swapi_density-of-neutral-helium-lut_20241023_v002.dat"
+      "imap_swapi_hydrogen-inflow-vector_20100101_v001.dat"
     ]
   },
   {
     "type": "ancillary",
     "files": [
-      "imap_swapi_helium-inflow-vector_20100101_v001.dat"
+      "imap_swapi_azimuthal-transmission_20260425_v001.csv"
     ]
   },
   {
     "type": "ancillary",
     "files": [
-      "imap_swapi_hydrogen-inflow-vector_20100101_v001.dat"
+      "imap_swapi_central-effective-area_20260425_v001.csv"
+    ]
+  },
+  {
+    "type": "ancillary",
+    "files": [
+      "imap_swapi_passband-fit-coefficients_20260425_v001.csv"
     ]
   }
 ]
\ No newline at end of file
diff --git a/scripts/swapi/reference_integral.py b/scripts/swapi/reference_integral.py
new file mode 100644
index 000000000..25af51271
--- /dev/null
+++ b/scripts/swapi/reference_integral.py
@@ -0,0 +1,358 @@
+"""Numba reference for the SWAPI proton solar wind integral with fixed limits.
+
+Mirrors the physics in `imap_l3_processing/swapi/l3a/science/solar_wind/forward_model.py::calculate_integral`
+but on a fixed dense trapezoid grid (no dynamic angular/speed windowing). Used
+as a ground-truth reference for the production GL quadrature in
+`docs/swapi/figure_src/build_validation_table.py` and `plot_spectra.py`.
+
+Fixed integration grid:
+  elevation:  -15 to 15 deg @ 0.05° (601 pts)
+  azimuth SG: -20 to 20 deg @ 0.05° (801 pts)
+  azimuth OA: 0.05° in transition |az| ∈ [20, 30], 0.5° to ±150° (441 pts/side)
+              — split at the |az| < 20° dead band so trapezoid doesn't
+                bridge the gap.
+  speed:      200 samples from 0.9 to 1.1 × central_speed
+
+The 0.05° elevation/SG-azimuth spacing was chosen to match the bilinear-
+interpolated integrand: cold plasma (vth ~10 km/s, σ_el = vth/v_b ≈ 0.4°) at
+SG passband elevation edges produces a near-cliff in the integrand, where
+0.1° trap undersampled the second derivative and biased the integral low by
+~1.4% (worst-case at bulk_el just outside SG el range).
+"""
+
+import math
+
+import numpy as np
+from numba import njit, prange
+
+from imap_l3_processing.swapi.l3a.science.solar_wind import params
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import bulk_speed
+from imap_l3_processing.swapi.response.swapi_response import ResponseGrid
+
+_EL = np.linspace(-15.0, 15.0, 601)
+_AZ_SG = np.linspace(-20.0, 20.0, 801)
+_AZ_OA_NEG = np.concatenate(
+    [np.arange(-150.0, -30.0, 0.5), np.linspace(-30.0, -20.0, 201)]
+)
+_AZ_OA_POS = np.concatenate([np.linspace(20.0, 30.0, 201), np.arange(30.5, 151.0, 0.5)])
+_SP_RATIO = np.linspace(0.9, 1.1, 200)
+
+
+def _trap_weights(x):
+    """Per-point trapezoid weights on a 1-D grid (handles non-uniform spacing)."""
+    w = np.empty_like(x)
+    w[0] = 0.5 * (x[1] - x[0])
+    w[-1] = 0.5 * (x[-1] - x[-2])
+    w[1:-1] = 0.5 * (x[2:] - x[:-2])
+    return w
+
+
+_EL_W = _trap_weights(_EL)
+_AZ_SG_W = _trap_weights(_AZ_SG)
+_AZ_OA_NEG_W = _trap_weights(_AZ_OA_NEG)
+_AZ_OA_POS_W = _trap_weights(_AZ_OA_POS)
+_SP_RATIO_W = _trap_weights(_SP_RATIO)
+
+
+@njit(fastmath=True, inline="always")
+def _interpolate_passband(values, min_el, el_sp, min_r, r_sp, el, speed_ratio):
+    """Bilinear on the (elevation, speed_ratio) passband grid; mirrors
+    `passband_grid._bilinear_interpolate`."""
+    i = (el - min_el) / el_sp
+    j = (speed_ratio - min_r) / r_sp
+    if i < 0 or i + 1 >= values.shape[0] or j < 0 or j + 1 >= values.shape[1]:
+        return 0.0
+    i0, j0 = int(i), int(j)
+    wi, wj = i - i0, j - j0
+    return (1 - wi) * ((1 - wj) * values[i0, j0] + wj * values[i0, j0 + 1]) + wi * (
+        (1 - wj) * values[i0 + 1, j0] + wj * values[i0 + 1, j0 + 1]
+    )
+
+
+@njit(fastmath=True, inline="always")
+def _interpolate_transmission(transmission, transmission_spacing, az):
+    """Mirror of `azimuthal_transmission.interpolate_azimuthal_transmission`."""
+    az = (az + 180.0) % 360.0 - 180.0
+    f = abs(az) / transmission_spacing
+    n = transmission.shape[0]
+    i0 = int(f)
+    if i0 < 0:
+        i0 = 0
+    elif i0 >= n:
+        i0 = n - 1
+    i1 = i0 + 1 if i0 + 1 < n else n - 1
+    return transmission[i0] * (i1 - f) + transmission[i1] * (f - i0)
+
+
+@njit(fastmath=True)
+def _integrate_region(
+    passband_values,
+    rotation,
+    bulk_r,
+    bulk_t,
+    bulk_n,
+    bulk_speed_value,
+    inv_two_sigma_sq,
+    central_speed,
+    transmission,
+    transmission_spacing,
+    pb_min_el,
+    pb_el_sp,
+    pb_min_r,
+    pb_r_sp,
+    el_points,
+    el_weights,
+    az_points,
+    az_weights,
+    sp_ratio_points,
+    sp_ratio_weights,
+):
+    bulk_speed_squared = bulk_speed_value * bulk_speed_value
+    total = 0.0
+    for i_el in range(el_points.shape[0]):
+        el = el_points[i_el]
+        sin_el = math.sin(math.radians(el))
+        cos_el = math.cos(math.radians(el))
+        el_weight = el_weights[i_el] * cos_el
+        for i_az in range(az_points.shape[0]):
+            az = az_points[i_az]
+            sin_az = math.sin(math.radians(az))
+            cos_az = math.cos(math.radians(az))
+            direction_x = -cos_el * sin_az
+            direction_y = -cos_el * cos_az
+            direction_z = -sin_el
+            direction_r = (
+                rotation[0, 0] * direction_x
+                + rotation[0, 1] * direction_y
+                + rotation[0, 2] * direction_z
+            )
+            direction_t = (
+                rotation[1, 0] * direction_x
+                + rotation[1, 1] * direction_y
+                + rotation[1, 2] * direction_z
+            )
+            direction_n = (
+                rotation[2, 0] * direction_x
+                + rotation[2, 1] * direction_y
+                + rotation[2, 2] * direction_z
+            )
+            bulk_along_direction = (
+                direction_r * bulk_r + direction_t * bulk_t + direction_n * bulk_n
+            )
+            transmission_at_az = _interpolate_transmission(
+                transmission, transmission_spacing, az
+            )
+            az_weight = az_weights[i_az] * transmission_at_az
+            for i_sp in range(sp_ratio_points.shape[0]):
+                speed_ratio = sp_ratio_points[i_sp]
+                speed = central_speed * speed_ratio
+                passband = _interpolate_passband(
+                    passband_values,
+                    pb_min_el,
+                    pb_el_sp,
+                    pb_min_r,
+                    pb_r_sp,
+                    el,
+                    speed_ratio,
+                )
+                exponent = (
+                    speed * speed
+                    + bulk_speed_squared
+                    - 2.0 * speed * bulk_along_direction
+                ) * inv_two_sigma_sq
+                integrand = passband * speed**3 * math.exp(-exponent)
+                speed_weight = central_speed * sp_ratio_weights[i_sp]
+                total += el_weight * az_weight * speed_weight * integrand
+    return total
+
+
+@njit(parallel=True, fastmath=True)
+def _batch(
+    sg_values_all,
+    oa_values_all,
+    rotations,
+    bulk_velocities_rtn,
+    bulk_speeds,
+    thermal_speeds,
+    densities,
+    central_speeds,
+    central_effective_areas,
+    transmission,
+    transmission_spacing,
+    pb_min_el,
+    pb_el_sp,
+    pb_min_r,
+    pb_r_sp,
+    el_points,
+    el_weights,
+    az_sg_points,
+    az_sg_weights,
+    az_oa_neg_points,
+    az_oa_neg_weights,
+    az_oa_pos_points,
+    az_oa_pos_weights,
+    sp_ratio_points,
+    sp_ratio_weights,
+):
+    n = sg_values_all.shape[0]
+    out = np.empty(n)
+    for i in prange(n):
+        sigma = thermal_speeds[i]
+        inv_two_sigma_sq = 1.0 / (2.0 * sigma * sigma)
+        bulk_r = bulk_velocities_rtn[i, 0]
+        bulk_t = bulk_velocities_rtn[i, 1]
+        bulk_n = bulk_velocities_rtn[i, 2]
+        bulk_speed_value = bulk_speeds[i]
+        rotation = rotations[i]
+        central_speed = central_speeds[i]
+
+        sg_total = _integrate_region(
+            sg_values_all[i],
+            rotation,
+            bulk_r,
+            bulk_t,
+            bulk_n,
+            bulk_speed_value,
+            inv_two_sigma_sq,
+            central_speed,
+            transmission,
+            transmission_spacing,
+            pb_min_el,
+            pb_el_sp,
+            pb_min_r,
+            pb_r_sp,
+            el_points,
+            el_weights,
+            az_sg_points,
+            az_sg_weights,
+            sp_ratio_points,
+            sp_ratio_weights,
+        )
+        oa_neg_total = _integrate_region(
+            oa_values_all[i],
+            rotation,
+            bulk_r,
+            bulk_t,
+            bulk_n,
+            bulk_speed_value,
+            inv_two_sigma_sq,
+            central_speed,
+            transmission,
+            transmission_spacing,
+            pb_min_el,
+            pb_el_sp,
+            pb_min_r,
+            pb_r_sp,
+            el_points,
+            el_weights,
+            az_oa_neg_points,
+            az_oa_neg_weights,
+            sp_ratio_points,
+            sp_ratio_weights,
+        )
+        oa_pos_total = _integrate_region(
+            oa_values_all[i],
+            rotation,
+            bulk_r,
+            bulk_t,
+            bulk_n,
+            bulk_speed_value,
+            inv_two_sigma_sq,
+            central_speed,
+            transmission,
+            transmission_spacing,
+            pb_min_el,
+            pb_el_sp,
+            pb_min_r,
+            pb_r_sp,
+            el_points,
+            el_weights,
+            az_oa_pos_points,
+            az_oa_pos_weights,
+            sp_ratio_points,
+            sp_ratio_weights,
+        )
+        rate = sg_total + oa_neg_total + oa_pos_total
+        # Matches `count_rate_conversion_factor` in solar_wind/utils.py.
+        prefactor = (
+            central_effective_areas[i]
+            * densities[i]
+            * (math.sqrt(2.0 * math.pi) * sigma) ** -3
+            * (math.pi / 180.0) ** 2
+            * 1e5
+        )
+        out[i] = rate * prefactor
+    return out
+
+
+def reference_integrals_batch(response_grids, sws, rotation_matrices):
+    """Compute N reference integrals in parallel via numba JIT.
+
+    Each `response_grids[i]` carries its sample's SG/OA passbands, central speed,
+    central effective area, and (the shared) azimuthal-transmission table —
+    matching the signature of `calculate_integral`. `rotation_matrices` (shape
+    (N, 3, 3)) map SWAPI XYZ → RTN per sample.
+    """
+    n = len(sws)
+    if n == 0:
+        return np.empty(0)
+    first_grid = response_grids[0]
+    first_sg = first_grid.sg_passband
+    nel, nsp = first_sg.values.shape
+    sg_values_all = np.empty((n, nel, nsp))
+    oa_values_all = np.empty((n, nel, nsp))
+    rotations = np.ascontiguousarray(rotation_matrices, dtype=np.float64)
+    bulk_velocities_rtn = np.empty((n, 3))
+    bulk_speeds = np.empty(n)
+    thermal_speeds = np.empty(n)
+    densities = np.empty(n)
+    central_speeds = np.empty(n)
+    central_effective_areas = np.empty(n)
+    for i in range(n):
+        rg = response_grids[i]
+        sg_values_all[i] = rg.sg_passband.values
+        oa_values_all[i] = rg.oa_passband.values
+        central_speeds[i] = rg.central_speed
+        central_effective_areas[i] = rg.central_effective_area
+        bulk_velocities_rtn[i] = sws[i].bulk_velocity_rtn
+        bulk_speeds[i] = bulk_speed(sws[i])
+        thermal_speeds[i] = params.thermal_speed(sws[i])
+        densities[i] = sws[i].density
+    transmission = np.ascontiguousarray(
+        first_grid.azimuthal_transmission.values, dtype=np.float64
+    )
+    return _batch(
+        sg_values_all,
+        oa_values_all,
+        rotations,
+        bulk_velocities_rtn,
+        bulk_speeds,
+        thermal_speeds,
+        densities,
+        central_speeds,
+        central_effective_areas,
+        transmission,
+        float(first_grid.azimuthal_transmission.spacing),
+        float(first_sg.min_elevation),
+        float(first_sg.elevation_spacing),
+        float(first_sg.min_speed_ratio),
+        float(first_sg.speed_ratio_spacing),
+        _EL,
+        _EL_W,
+        _AZ_SG,
+        _AZ_SG_W,
+        _AZ_OA_NEG,
+        _AZ_OA_NEG_W,
+        _AZ_OA_POS,
+        _AZ_OA_POS_W,
+        _SP_RATIO,
+        _SP_RATIO_W,
+    )
+
+
+def reference_integral_fixed_limits(
+    response_grid: ResponseGrid, sw, rotation_matrix
+) -> float:
+    """Single-sample ground truth (delegates to the parallel batch)."""
+    rm = np.asarray(rotation_matrix, dtype=np.float64).reshape(1, 3, 3)
+    return float(reference_integrals_batch([response_grid], [sw], rm)[0])
diff --git a/scripts/swapi/sample_wind_solar_wind.py b/scripts/swapi/sample_wind_solar_wind.py
new file mode 100644
index 000000000..83fa042b0
--- /dev/null
+++ b/scripts/swapi/sample_wind_solar_wind.py
@@ -0,0 +1,242 @@
+#!/usr/bin/env python3
+"""
+Sample real solar wind conditions from WIND/SWE 2-min ASCII data.
+
+Downloads the WIND/SWE 2-min "all_orbit_phases"-excluded ASCII file for a given
+year (default 2025), filters for high-quality bimaxwellian fits (fit_flag == 10
+unless --keep-flags is given), drops rows with fill values in the quantities we
+care about, randomly samples N rows, and writes a CSV usable as ground-truth
+solar wind parameters for plot_fit_accuracy.py and similar scripts.
+
+Source: https://spdf.gsfc.nasa.gov/pub/data/wind/swe/ascii/2-min/
+
+Frame note: WIND publishes proton velocity in GSE. We add an approximate RTN
+projection (v_R = -Vx_GSE, v_T = -Vy_GSE, v_N = Vz_GSE) which is accurate at
+L1 to within the ~7° tilt between GSE-Z and the solar rotation axis — fine for
+sampling realistic bulk_speed / vT / vN ranges.
+
+Output columns (units in header row):
+  year, fdoy, fit_flag,
+  proton_bulk_speed_km_s, proton_thermal_speed_km_s, proton_density_cm3,
+  proton_vx_gse_km_s, proton_vy_gse_km_s, proton_vz_gse_km_s,
+  v_R_km_s, v_T_km_s, v_N_km_s, proton_temperature_K
+
+Usage:
+  conda run -n imapL3 python scripts/swapi/sample_wind_solar_wind.py
+  conda run -n imapL3 python scripts/swapi/sample_wind_solar_wind.py \
+      --year 2025 --n 1000 --seed 7 \
+      --output docs/swapi/figure_src/wind_solar_wind_samples_2025.csv
+"""
+
+import argparse
+import sys
+import urllib.request
+from pathlib import Path
+
+import numpy as np
+
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(_REPO_ROOT))
+
+from imap_l3_processing.constants import (
+    BOLTZMANN_CONSTANT_JOULES_PER_KELVIN,
+    PROTON_MASS_KG,
+)
+
+_BASE_URL = "https://spdf.gsfc.nasa.gov/pub/data/wind/swe/ascii/2-min"
+_DEFAULT_CACHE_DIR = Path.home() / ".cache" / "imap_l3" / "wind_swe_2m"
+_DEFAULT_OUTPUT = (
+    _REPO_ROOT / "docs" / "swapi" / "figure_src" / "wind_solar_wind_samples_2025.csv"
+)
+
+# Column indices (0-based) into the WIND ASCII record. See documentation_jck.txt
+# at the source URL for the full layout.
+_COL_YEAR = 0
+_COL_FDOY = 1
+_COL_FIT_FLAG = 2
+_COL_VBULK = 3  # proton bulk speed [km/s]
+_COL_VX_GSE = 5  # proton Vx GSE [km/s]
+_COL_VY_GSE = 7
+_COL_VZ_GSE = 9
+_COL_W_SCALAR = 11  # proton scalar thermal speed [km/s]
+_COL_NP = 21  # proton density [cm^-3]
+
+# WIND fill values used in this dataset.
+_FILL_F7 = 99999.9
+_FILL_F9 = 99999.999
+
+
+def _download(year: int, cache_dir: Path) -> Path:
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    name = f"wind_swe_2m_sw{year}.asc"
+    dst = cache_dir / name
+    if dst.exists() and dst.stat().st_size > 0:
+        print(f"Using cached {dst} ({dst.stat().st_size / 1e6:.1f} MB)")
+        return dst
+    url = f"{_BASE_URL}/{name}"
+    print(f"Downloading {url} -> {dst}")
+    tmp = dst.with_suffix(dst.suffix + ".part")
+    with urllib.request.urlopen(url) as resp, open(tmp, "wb") as out:
+        size = int(resp.headers.get("Content-Length", "0"))
+        chunk = 1 << 20
+        bytes_read = 0
+        while True:
+            buf = resp.read(chunk)
+            if not buf:
+                break
+            out.write(buf)
+            bytes_read += len(buf)
+            if size:
+                pct = 100.0 * bytes_read / size
+                print(
+                    f"  {bytes_read / 1e6:7.1f} / {size / 1e6:.1f} MB ({pct:5.1f}%)",
+                    end="\r",
+                )
+        print()
+    tmp.rename(dst)
+    return dst
+
+
+def _load(path: Path) -> np.ndarray:
+    """Load the WIND 2-min ASCII file. Comment-prefixed lines start with ';'."""
+    print(f"Parsing {path} ...")
+    data = np.loadtxt(path, comments=";")
+    print(f"  {data.shape[0]} rows, {data.shape[1]} columns")
+    return data
+
+
+def _thermal_speed_to_temperature_K(w_km_s: np.ndarray) -> np.ndarray:
+    """Convert scalar thermal speed (km/s) to temperature (K) for protons.
+
+    Using w_th = sqrt(2 k T / m), so T = m w_th^2 / (2 k).
+    """
+    w_m_s = w_km_s * 1e3
+    return PROTON_MASS_KG * w_m_s**2 / (2.0 * BOLTZMANN_CONSTANT_JOULES_PER_KELVIN)
+
+
+def main():
+    p = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
+    p.add_argument("--year", type=int, default=2025)
+    p.add_argument("--n", type=int, default=1000, help="Number of samples to draw")
+    p.add_argument("--seed", type=int, default=7)
+    p.add_argument(
+        "--keep-flags",
+        type=int,
+        nargs="+",
+        default=[10],
+        help="Fit flags to keep (default: 10 = ideal proton fits). "
+        "See documentation_jck.txt at the source URL.",
+    )
+    p.add_argument("--cache-dir", type=Path, default=_DEFAULT_CACHE_DIR)
+    p.add_argument("--output", type=Path, default=_DEFAULT_OUTPUT)
+    p.add_argument(
+        "--no-cache-download",
+        action="store_true",
+        help="Skip download even if the cached file is missing (errors instead).",
+    )
+    args = p.parse_args()
+
+    src = args.cache_dir / f"wind_swe_2m_sw{args.year}.asc"
+    if not src.exists():
+        if args.no_cache_download:
+            sys.exit(f"Missing cached file {src} and --no-cache-download was set.")
+        src = _download(args.year, args.cache_dir)
+
+    data = _load(src)
+
+    fit_flag = data[:, _COL_FIT_FLAG].astype(int)
+    keep = np.isin(fit_flag, np.asarray(args.keep_flags, dtype=int))
+    print(f"  After fit-flag filter (flags={args.keep_flags}): {keep.sum()} rows")
+
+    # Drop rows with fill values in the proton quantities we sample.
+    cols_required = [
+        _COL_VBULK,
+        _COL_VX_GSE,
+        _COL_VY_GSE,
+        _COL_VZ_GSE,
+        _COL_W_SCALAR,
+        _COL_NP,
+    ]
+    finite = np.ones(data.shape[0], dtype=bool)
+    for c in cols_required:
+        col = data[:, c]
+        # Both fill values appear in this dataset; treat anything ≥ 9.99e4 as fill.
+        finite &= np.isfinite(col) & (np.abs(col) < 9e4)
+    keep &= finite
+    print(f"  After fill-value filter: {keep.sum()} rows")
+
+    if keep.sum() < args.n:
+        sys.exit(
+            f"Only {keep.sum()} rows survive filters but {args.n} samples requested."
+        )
+
+    rng = np.random.default_rng(args.seed)
+    candidates = np.flatnonzero(keep)
+    chosen = rng.choice(candidates, size=args.n, replace=False)
+    chosen.sort()
+    sub = data[chosen]
+
+    year = sub[:, _COL_YEAR].astype(int)
+    fdoy = sub[:, _COL_FDOY]
+    flag = sub[:, _COL_FIT_FLAG].astype(int)
+    v_bulk = sub[:, _COL_VBULK]
+    vx_gse = sub[:, _COL_VX_GSE]
+    vy_gse = sub[:, _COL_VY_GSE]
+    vz_gse = sub[:, _COL_VZ_GSE]
+    w_scalar = sub[:, _COL_W_SCALAR]
+    n_p = sub[:, _COL_NP]
+
+    # GSE -> RTN approximation valid at L1 (within ~7° tilt between GSE-Z and
+    # the solar rotation axis). Sufficient for sampling realistic SW ranges.
+    v_R = -vx_gse
+    v_T = -vy_gse
+    v_N = vz_gse
+
+    T_K = _thermal_speed_to_temperature_K(w_scalar)
+
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    header = (
+        "year,fdoy,fit_flag,"
+        "proton_bulk_speed_km_s,proton_thermal_speed_km_s,proton_density_cm3,"
+        "proton_vx_gse_km_s,proton_vy_gse_km_s,proton_vz_gse_km_s,"
+        "v_R_km_s,v_T_km_s,v_N_km_s,proton_temperature_K"
+    )
+    out = np.column_stack(
+        [
+            year,
+            fdoy,
+            flag,
+            v_bulk,
+            w_scalar,
+            n_p,
+            vx_gse,
+            vy_gse,
+            vz_gse,
+            v_R,
+            v_T,
+            v_N,
+            T_K,
+        ]
+    )
+    fmt = "%d,%.6f,%d,%.3f,%.3f,%.4f,%.3f,%.3f,%.3f,%.3f,%.3f,%.3f,%.3e"
+    np.savetxt(args.output, out, fmt=fmt, header=header, comments="")
+    print(f"Wrote {args.n} samples -> {args.output}")
+
+    print("\nSummary of sampled distributions:")
+    for label, arr, unit in [
+        ("bulk_speed", v_bulk, "km/s"),
+        ("thermal_speed", w_scalar, "km/s"),
+        ("temperature", T_K, "K"),
+        ("density", n_p, "cm^-3"),
+        ("v_R", v_R, "km/s"),
+        ("v_T", v_T, "km/s"),
+        ("v_N", v_N, "km/s"),
+    ]:
+        q = np.percentile(arr, [1, 50, 99])
+        print(
+            f"  {label:>13s} [{unit}]  p1={q[0]:9.3g}  median={q[1]:9.3g}  p99={q[2]:9.3g}"
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/swapi/swapi_proton_sw_speed_demo.py b/scripts/swapi/swapi_proton_sw_speed_demo.py
deleted file mode 100644
index 7579afc2f..000000000
--- a/scripts/swapi/swapi_proton_sw_speed_demo.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import os
-
-import numpy as np
-from matplotlib import pyplot as plt
-from spacepy.pycdf import CDF
-from uncertainties.unumpy import uarray, nominal_values, std_devs
-
-from imap_l3_processing.swapi.l3a.models import SwapiL2Data
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_clock_and_deflection_angles import \
-    calculate_clock_angle, calculate_deflection_angle, ClockAngleCalibrationTable
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_speed import sine_fit_function, \
-    calculate_proton_solar_wind_speed, calculate_proton_centers_of_mass, extract_coarse_sweep
-
-
-def read_l2_data_from_dat(file_path: str) -> SwapiL2Data:
-    data = np.loadtxt(file_path)
-    data = data.reshape((-1, 72, 8))
-
-    twelve_seconds_in_nanoseconds = 12_000_000_000
-    epochs = twelve_seconds_in_nanoseconds * np.arange(len(data))
-
-    energy_for_first_sweep = data[0, :, 2]
-    spin_angles = data[..., 3]
-    coarse_sweep_energies = data[:, 1:63, 2]
-    assert np.all(coarse_sweep_energies == coarse_sweep_energies[0])
-
-    coincident_count_rates = data[..., 7]
-
-    fake_coincident_count_rate_uncertainties = np.sqrt(6 * coincident_count_rates)
-    return SwapiL2Data(epochs,
-                       energy_for_first_sweep,
-                       coincident_count_rates,
-                       spin_angles,
-                       fake_coincident_count_rate_uncertainties)
-
-
-def read_l2_data(cdf_path: str) -> SwapiL2Data:
-    cdf = CDF(cdf_path)
-    return SwapiL2Data(cdf.raw_var("epoch")[...],
-                       cdf["energy"][...],
-                       cdf["swp_coin_rate"][...],
-                       cdf["spin_angles"][...],
-                       cdf["swp_coin_unc"][...])
-
-
-fig = plt.figure()
-
-
-def plot_sweeps(data):
-    for i in range(len(data.sci_start_time)):
-        axes = fig.add_subplot(len(data.sci_start_time) + 1, 1, i + 1)
-        axes.loglog(data.energy, data.coincidence_count_rate[i, :], marker='.', linestyle="None")
-        axes.set(xlabel="Energy", ylabel="Count Rate")
-
-
-def plot_variation_in_center_of_mass(a, phi, b, spin_angles, centers_of_mass):
-    fit_xs = np.arange(0, 360, 3)
-    fit_ys = sine_fit_function(fit_xs, a.n, phi.n % 360, b.n)
-    plot = fig.add_subplot(len(spin_angles) + 1, 1, len(spin_angles) + 1)
-
-    plot.errorbar(spin_angles, nominal_values(centers_of_mass), yerr=std_devs(centers_of_mass), fmt=".")
-
-    plot.plot(fit_xs, fit_ys)
-    plot.set(xlabel="Phase Angle", ylabel="Energy")
-
-
-def run_example_dat_files():
-    data = read_l2_data_from_dat("../../instrument_team_data/swapi/swapi_test_data_v4.dat")
-    coincident_count_rate = uarray(data.coincidence_count_rate, data.coincidence_count_rate_uncertainty)
-    proton_sw_speed, a, phi, b = calculate_proton_solar_wind_speed(coincident_count_rate, data.spin_angles, data.energy,
-                                                                   data.sci_start_time)
-
-    clock_angle_lut = ClockAngleCalibrationTable.from_file('../../tests/test_data/swapi/example_LUT_flow_angle_v2.dat')
-
-    clock_angle = calculate_clock_angle(clock_angle_lut, proton_sw_speed, a, phi, b)
-
-    deflection_angle = calculate_deflection_angle(clock_angle_lut, proton_sw_speed, a, phi, b)
-
-    print(f"clock: {clock_angle}")
-    print(f"deflection angle: {deflection_angle}")
-
-
-def main(file_path):
-    if file_path[-4:] == ".dat":
-        data = read_l2_data_from_dat(file_path)
-    elif file_path[-4:] == ".cdf":
-        data = read_l2_data(os.path.abspath(file_path))
-    else:
-        raise Exception("The demo can only load data from .dat or .cdf files!")
-
-    coincident_count_rate = uarray(data.coincidence_count_rate, data.coincidence_count_rate_uncertainty)
-
-    plot_sweeps(data)
-
-    centers_of_mass, spin_angles = calculate_proton_centers_of_mass(
-        extract_coarse_sweep(coincident_count_rate),
-        extract_coarse_sweep(data.spin_angles),
-        extract_coarse_sweep(data.energy),
-        data.sci_start_time)
-    proton_sw_speed, a, phi, b = calculate_proton_solar_wind_speed(coincident_count_rate, data.spin_angles, data.energy,
-                                                                   data.sci_start_time)
-
-    plot_variation_in_center_of_mass(a, phi, b, spin_angles, centers_of_mass)
-
-    print(f"SW H+ speed: {proton_sw_speed}")
-    print(f"SW H+ clock angle: {phi}")
-    print(f"A {a}")
-    print(f"B {b}")
-    print(f"A/B {a / b}")
-
-    if __name__ == "__main__":
-        fig.legend([f"SW H+ speed: {proton_sw_speed :.3f}",
-                    f"SW H+ clock angle: {phi:.3f}",
-                    f"A: {a:.3f}",
-                    f"B: {b:.3f}",
-                    f"A/B: {(a / b):.5f}"],
-                   )
-
-        fig.set_figheight(10)
-        plt.show()
-
-
-if __name__ == "__main__":
-    run_example_dat_files()
-    # try:
-    #     file_path = sys.argv[1]
-    #     main(file_path)
-    # except:
-    #     main(os.path.abspath("test_data/imap_swapi_l2_fake-menlo-5-sweeps_20100101_v002.cdf"))
diff --git a/scripts/swapi/upload_swapi_l3_dependencies.py b/scripts/swapi/upload_swapi_l3_dependencies.py
index 78ae520f6..acec763f6 100644
--- a/scripts/swapi/upload_swapi_l3_dependencies.py
+++ b/scripts/swapi/upload_swapi_l3_dependencies.py
@@ -4,26 +4,28 @@
 import imap_data_access
 from imap_data_access.file_validation import generate_imap_file_path
 
-from tests.test_helpers import get_test_data_path
+from tests.test_helpers import get_test_data_path, get_test_instrument_team_data_path
 
+_swapi = get_test_data_path('swapi')
 files_to_upload = [
-    "imap_swapi_alpha-density-temperature-lut_20240920_v000.dat",
-    "imap_swapi_clock-angle-and-flow-deflection-lut_20240918_v000.dat",
-    "imap_swapi_density-of-neutral-helium-lut_20241023_v000.dat",
-    "imap_swapi_proton-density-temperature-lut_20240905_v000.dat",
-    "imap_swapi_efficiency-lut_20241020_v000.dat",
-    "imap_swapi_energy-gf-pui-lut_20100101_v001.csv",
-    "imap_swapi_energy-gf-sw-lut_20100101_v001.csv",
-    "imap_swapi_instrument-response-lut_20241023_v000.zip",
+    _swapi / "imap_swapi_alpha-density-temperature-lut_20240920_v000.dat",
+    _swapi / "imap_swapi_density-of-neutral-helium-lut_20241023_v000.dat",
+    _swapi / "imap_swapi_efficiency-lut_20241020_v000.dat",
+    _swapi / "imap_swapi_energy-gf-pui-lut_20100101_v001.csv",
+    _swapi / "imap_swapi_energy-gf-sw-lut_20100101_v001.csv",
+    _swapi / "imap_swapi_instrument-response-lut_20241023_v000.zip",
+    get_test_instrument_team_data_path("swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv"),
+    get_test_instrument_team_data_path("swapi/imap_swapi_central-effective-area_20260425_v001.csv"),
+    get_test_instrument_team_data_path("swapi/imap_swapi_passband-fit-coefficients_20260425_v001.csv"),
 ]
 upload_to_local_dir = False
 for file in files_to_upload:
     try:
         if upload_to_local_dir:
-            write_path = generate_imap_file_path(file).construct_path()
-            shutil.copy(get_test_data_path('swapi') / file, write_path)
+            write_path = generate_imap_file_path(file.name).construct_path()
+            shutil.copy(file, write_path)
         else:
-            imap_data_access.upload(Path(__file__).parent.parent / "swapi" / "test_data" / file)
-            print("Successfully uploaded", file)
+            imap_data_access.upload(file)
+            print("Successfully uploaded", file.name)
     except Exception as e:
-        print(f'File "{file}" failed to upload with exception {e}')
+        print(f'File "{file.name}" failed to upload with exception {e}')
diff --git a/scripts/swe/create_fake_l2_cdf.py b/scripts/swe/create_fake_l2_cdf.py
index 6e4f57b6d..c3fa5480e 100644
--- a/scripts/swe/create_fake_l2_cdf.py
+++ b/scripts/swe/create_fake_l2_cdf.py
@@ -119,10 +119,6 @@ def create_fake_swapi_l3a_cdf(l2_swepam_ion_file_path: str,
         "vel_p_rtn")._index
     sw_velocity_data = np.array([x[sw_velocity_index] for x in vd_ion[:]])
     sw_speed = np.linalg.norm(sw_velocity_data, axis=1)
-    sw_r, sw_t, sw_n = sw_velocity_data.T
-    sw_x, sw_y, sw_z = sw_n, sw_t, -sw_r
-    deflection = np.rad2deg(np.arctan2(np.sqrt(sw_x ** 2 + sw_y ** 2), -sw_z))
-    clock = np.rad2deg(np.arctan2(sw_x, -sw_y))
 
     epochs = get_epochs_from_output_file(vd_ion)
 
@@ -134,10 +130,6 @@ def create_fake_swapi_l3a_cdf(l2_swepam_ion_file_path: str,
         cdf['epoch_delta'] = epoch_deltas
         cdf['proton_sw_speed'] = sw_speed
         cdf['proton_sw_speed'].attrs["FILLVAL"] = -1e31
-        cdf['proton_sw_clock_angle'] = clock
-        cdf['proton_sw_clock_angle'].attrs["FILLVAL"] = -1e31
-        cdf['proton_sw_deflection_angle'] = deflection
-        cdf['proton_sw_deflection_angle'].attrs["FILLVAL"] = -1e31
 
 
 ATTITUDE_DATA = """Year	DOY	Secofday	Flag	RTN_r		RTN_t		RTN_n		J2GCI_x		J2GCI_y		J2GCI_z		GSE_x		GSE_y		GSE_z
diff --git a/tests/cdf/test_imap_attribute_manager.py b/tests/cdf/test_imap_attribute_manager.py
index 5f597587b..f43ad1025 100644
--- a/tests/cdf/test_imap_attribute_manager.py
+++ b/tests/cdf/test_imap_attribute_manager.py
@@ -39,14 +39,6 @@ def test_load_instrument_and_variable_attributes_with_level(self):
                          manager.get_variable_attributes('proton_sw_speed_uncert'))
         self.assertEqual(self.base_manager.get_variable_attributes('proton_sw_speed'),
                          manager.get_variable_attributes('proton_sw_speed'))
-        self.assertEqual(self.base_manager.get_variable_attributes('proton_sw_clock_angle'),
-                         manager.get_variable_attributes('proton_sw_clock_angle'))
-        self.assertEqual(self.base_manager.get_variable_attributes('proton_sw_clock_angle_uncert'),
-                         manager.get_variable_attributes('proton_sw_clock_angle_uncert'))
-        self.assertEqual(self.base_manager.get_variable_attributes('proton_sw_deflection_angle'),
-                         manager.get_variable_attributes('proton_sw_deflection_angle'))
-        self.assertEqual(self.base_manager.get_variable_attributes('proton_sw_deflection_angle_uncert'),
-                         manager.get_variable_attributes('proton_sw_deflection_angle_uncert'))
 
         self.assertEqual(self.base_manager.get_variable_attributes('alpha_sw_speed'),
                          manager.get_variable_attributes('alpha_sw_speed'))
diff --git a/tests/integration/integration_test_helpers.py b/tests/integration/integration_test_helpers.py
index c19957b33..1683f9a2d 100644
--- a/tests/integration/integration_test_helpers.py
+++ b/tests/integration/integration_test_helpers.py
@@ -70,6 +70,20 @@ def fake_download(file: Path | str):
     assert full_path.exists(), f"Expected {full_path} to exist, but it doesn't."
     return full_path
 
+
+def stage_input_file(file_path: Path) -> Path:
+    """Copy a fixture into its canonical path under ``imap_data_access.config['DATA_DIR']``.
+
+    Mirrors ``mock_imap_data_access.__enter__``'s staging behaviour as a one-shot
+    helper, for tests that hit the live SDC for most inputs but want to drop a
+    single locally-checked-in file (typically a dependency JSON) into the spot
+    ``imap_data_access.download`` resolves to.
+    """
+    destination = generate_imap_file_path(file_path.name).construct_path()
+    destination.parent.mkdir(parents=True, exist_ok=True)
+    shutil.copy(file_path, destination)
+    return destination
+
 metakernel_text = """
 \\begindata
 
@@ -160,9 +174,7 @@ def __enter__(self):
 
         for file_path in self.input_files:
             try:
-                paths_to_generate = generate_imap_file_path(file_path.name).construct_path()
-                paths_to_generate.parent.mkdir(exist_ok=True, parents=True)
-                shutil.copy(src=file_path, dst=paths_to_generate)
+                stage_input_file(file_path)
             except:
                 pass
 
diff --git a/tests/integration/test_data/swapi/imap_swapi_l3a_alpha-sw_20260101_v001.json b/tests/integration/test_data/swapi/imap_swapi_l3a_alpha-sw_20260101_v001.json
new file mode 100644
index 000000000..dee83cf83
--- /dev/null
+++ b/tests/integration/test_data/swapi/imap_swapi_l3a_alpha-sw_20260101_v001.json
@@ -0,0 +1,26 @@
+[
+  {"type": "science", "files": ["imap_swapi_l2_sci_20260101_v001.cdf"]},
+  {"type": "science", "files": ["imap_mag_l2_norm-rtn_20260101_v001.cdf"]},
+  {"type": "ancillary", "files": ["imap_swapi_alpha-density-temperature-lut_20250125_v001.dat"]},
+  {"type": "ancillary", "files": ["imap_swapi_efficiency-lut_20241020_v001.dat"]},
+  {"type": "ancillary", "files": ["imap_swapi_energy-gf-pui-lut_20100101_v003.csv"]},
+  {"type": "ancillary", "files": ["imap_swapi_instrument-response-lut_20241023_v001.zip"]},
+  {"type": "ancillary", "files": ["imap_swapi_density-of-neutral-helium-lut_20241023_v002.dat"]},
+  {"type": "ancillary", "files": ["imap_swapi_hydrogen-inflow-vector_20100101_v001.dat"]},
+  {"type": "ancillary", "files": ["imap_swapi_helium-inflow-vector_20100101_v001.dat"]},
+  {"type": "ancillary", "files": ["imap_swapi_azimuthal-transmission_20250924_v001.csv"]},
+  {"type": "ancillary", "files": ["imap_swapi_central-effective-area_20250924_v001.csv"]},
+  {"type": "ancillary", "files": ["imap_swapi_passband-fit-coefficients_20250924_v001.csv"]},
+  {"type": "spice", "files": [
+    "naif0012.tls",
+    "pck00011.tpc",
+    "imap_130.tf",
+    "imap_science_120.tf",
+    "imap_sclk_0161.tsc",
+    "de440.bsp",
+    "imap_recon_20250925_20260420_v01.bsp",
+    "imap_2025_358_2026_085_004.ah.bc",
+    "imap_dps_2025_363_2025_365_001.ah.bc",
+    "imap_dps_2025_359_2026_115_002.ah.bc"
+  ]}
+]
diff --git a/tests/integration/test_data/swapi/imap_swapi_l3a_proton-sw_20260101_v001.json b/tests/integration/test_data/swapi/imap_swapi_l3a_proton-sw_20260101_v001.json
new file mode 100644
index 000000000..994558c14
--- /dev/null
+++ b/tests/integration/test_data/swapi/imap_swapi_l3a_proton-sw_20260101_v001.json
@@ -0,0 +1,25 @@
+[
+  {"type": "science", "files": ["imap_swapi_l2_sci_20260101_v001.cdf"]},
+  {"type": "ancillary", "files": ["imap_swapi_alpha-density-temperature-lut_20250125_v001.dat"]},
+  {"type": "ancillary", "files": ["imap_swapi_efficiency-lut_20241020_v001.dat"]},
+  {"type": "ancillary", "files": ["imap_swapi_energy-gf-pui-lut_20100101_v003.csv"]},
+  {"type": "ancillary", "files": ["imap_swapi_instrument-response-lut_20241023_v001.zip"]},
+  {"type": "ancillary", "files": ["imap_swapi_density-of-neutral-helium-lut_20241023_v002.dat"]},
+  {"type": "ancillary", "files": ["imap_swapi_hydrogen-inflow-vector_20100101_v001.dat"]},
+  {"type": "ancillary", "files": ["imap_swapi_helium-inflow-vector_20100101_v001.dat"]},
+  {"type": "ancillary", "files": ["imap_swapi_azimuthal-transmission_20250924_v001.csv"]},
+  {"type": "ancillary", "files": ["imap_swapi_central-effective-area_20250924_v001.csv"]},
+  {"type": "ancillary", "files": ["imap_swapi_passband-fit-coefficients_20250924_v001.csv"]},
+  {"type": "spice", "files": [
+    "naif0012.tls",
+    "pck00011.tpc",
+    "imap_130.tf",
+    "imap_science_120.tf",
+    "imap_sclk_0161.tsc",
+    "de440.bsp",
+    "imap_recon_20250925_20260420_v01.bsp",
+    "imap_2025_358_2026_085_004.ah.bc",
+    "imap_dps_2025_363_2025_365_001.ah.bc",
+    "imap_dps_2025_359_2026_115_002.ah.bc"
+  ]}
+]
diff --git a/tests/integration/test_swapi_processor_integration.py b/tests/integration/test_swapi_processor_integration.py
new file mode 100644
index 000000000..74c0d2c4e
--- /dev/null
+++ b/tests/integration/test_swapi_processor_integration.py
@@ -0,0 +1,187 @@
+"""End-to-end subprocess integration tests for the SWAPI L3a processors.
+
+Mirrors test_swe_processor_integration.py: runs ``imap_l3_data_processor.py`` as
+a subprocess against staged test data, exercising the full real path —
+dependency manifest deserialization → SPICE furnishing → SwapiL3ADependencies
+loading of all 13 ancillaries → SwapiProcessor.process() → process_l3a_*
+→ save_data → CDF written to disk.
+
+SWAPI-specific inputs live in ``tests/integration/test_data/swapi/``. SPICE
+kernels are pulled from the shared ``tests/integration/test_data/spice/`` dir.
+The L2 science CDF is generated on the fly by retiming an existing synthetic
+spectrum into the SPICE coverage window — keeping a date-shifted copy on disk
+would just be duplicate data.
+"""
+
+import os
+import subprocess
+import sys
+import unittest
+from pathlib import Path
+from unittest import skipUnless
+
+import numpy as np
+import imap_data_access
+import numpy.testing
+from imap_data_access import ScienceFilePath
+from spacepy.pycdf import CDF
+import datetime
+
+import imap_l3_processing
+from tests.integration.integration_test_helpers import stage_input_file
+
+SWAPI_INTEGRATION_DATA_DIR = Path(__file__).parent / "test_data" / "swapi"
+
+
+class SwapiProcessorIntegration(unittest.TestCase):
+    @skipUnless(os.environ.get("IMAP_API_KEY"), "requires production API key")
+    def test_proton_sw_with_production_data(self):
+        """
+        With real data and with full dependency setup, validate that the CDF output
+        matches hardcoded expected values to check for unexpected changes
+        """
+
+        expected_values = {
+            'epoch': datetime.datetime(2025, 12, 31, 23, 59, 35, 6000),
+            'proton_sw_speed': 474.57455,
+            'proton_sw_speed_uncert': 0.36071813,
+            'proton_sw_speed_sun': 475.3688,
+            'proton_sw_speed_sun_uncert': 0.3539945,
+            'epoch_delta': 30000000000,
+            'proton_sw_temperature': 55131.13,
+            'proton_sw_temperature_uncert': 2008.7198,
+            'proton_sw_density': 2.6919234,
+            'proton_sw_density_uncert': 0.049627114,
+            'proton_sw_bulk_velocity_rtn_sun': [474.0841, 30.623661, 16.79102],
+            'proton_sw_bulk_velocity_rtn_sun_covariance': [[0.1169681, -0.0341877, 0.13108876],
+                                                           [-0.0341877, 0.8606747, -0.23923519],
+                                                           [0.13108876, -0.23923519, 1.3221142]],
+            'proton_sw_bulk_velocity_rtn_sc': [474.14252, 1.0705476, 20.217045],
+            'proton_sw_bulk_velocity_rtn_sc_covariance': [[0.1169681, -0.0341877, 0.13108876],
+                                                             [-0.0341877, 0.8606747, -0.23923519],
+                                                             [0.13108876, -0.23923519, 1.3221142]],
+            'swp_flags': 0
+        }
+
+        root_dir = Path(imap_l3_processing.__file__).parent.parent
+        os.chdir(root_dir)
+        imap_data_access.config["DATA_DIR"] = root_dir / "data"
+
+        dependency_filename = "imap_swapi_l3a_proton-sw_20260101_v001.json"
+        stage_input_file(SWAPI_INTEGRATION_DATA_DIR / dependency_filename)
+
+        expected_file_path = ScienceFilePath(
+            "imap_swapi_l3a_proton-sw_20260101_v001.cdf"
+        ).construct_path()
+        if expected_file_path.parent.exists():
+            expected_file_path.unlink(missing_ok=True)
+
+        result = subprocess.run(
+            [
+                sys.executable,
+                "imap_l3_data_processor.py",
+                "--instrument", "swapi",
+                "--data-level", "l3a",
+                "--descriptor", "proton-sw",
+                "--start-date", "20260101",
+                "--version", "v001",
+                "--dependency", dependency_filename,
+            ]
+        )
+
+        self.assertEqual(0, result.returncode)
+        self.assertTrue(expected_file_path.exists())
+
+
+        bulk_speed_atol = 1e-3 * float(expected_values['proton_sw_speed'])
+
+        with CDF(str(expected_file_path)) as cdf:
+            for key in expected_values.keys():
+                actual_value = cdf[key][0]
+                if 'bulk_velocity' in key:
+                    rtol, atol = 1e-3, bulk_speed_atol
+                elif key.endswith('_uncert'):
+                    rtol, atol = 1e-2, 0.0
+                else:
+                    rtol, atol = 1e-3, 0.0
+                try:
+                    numpy.testing.assert_allclose(
+                        actual_value, expected_values[key], rtol=rtol, atol=atol, err_msg=key
+                    )
+                except TypeError:
+                    self.assertEqual(expected_values[key], actual_value, msg=key)
+
+
+
+    @skipUnless(os.environ.get("IMAP_API_KEY"), "requires production API key")
+    def test_alpha_sw_with_production_data(self):
+        """
+        With real data and with full dependency setup, validate that the alpha-sw
+        CDF output matches hardcoded expected values to check for unexpected changes.
+        Unlike proton-sw, alpha-sw requires a MAG RTN science input (L2 preferred,
+        L1D fallback) for the field-aligned drift constraint.
+        """
+
+        # TODO: replace placeholders with values captured from a real run, matching
+        # the proton-sw pattern. Until then the test still guards subprocess success,
+        # CDF creation, and presence + finiteness of every alpha-sw variable.
+        expected_keys = [
+            'epoch',
+            'epoch_delta',
+            'alpha_sw_density',
+            'alpha_sw_density_uncert',
+            'alpha_sw_temperature',
+            'alpha_sw_temperature_uncert',
+            'alpha_sw_velocity_rtn',
+            'alpha_sw_velocity_covariance_rtn',
+            'alpha_sw_delta_v',
+            'alpha_sw_delta_v_uncert',
+            'alpha_sw_b_hat_rtn',
+            'swp_flags',
+        ]
+
+        root_dir = Path(imap_l3_processing.__file__).parent.parent
+        os.chdir(root_dir)
+        imap_data_access.config["DATA_DIR"] = root_dir / "data"
+
+        dependency_filename = "imap_swapi_l3a_alpha-sw_20260101_v001.json"
+        stage_input_file(SWAPI_INTEGRATION_DATA_DIR / dependency_filename)
+
+        expected_file_path = ScienceFilePath(
+            "imap_swapi_l3a_alpha-sw_20260101_v001.cdf"
+        ).construct_path()
+        if expected_file_path.parent.exists():
+            expected_file_path.unlink(missing_ok=True)
+
+        result = subprocess.run(
+            [
+                sys.executable,
+                "imap_l3_data_processor.py",
+                "--instrument", "swapi",
+                "--data-level", "l3a",
+                "--descriptor", "alpha-sw",
+                "--start-date", "20260101",
+                "--version", "v001",
+                "--dependency", dependency_filename,
+            ]
+        )
+
+        self.assertEqual(0, result.returncode)
+        self.assertTrue(expected_file_path.exists())
+
+        with CDF(str(expected_file_path)) as cdf:
+            for key in expected_keys:
+                self.assertIn(key, cdf, msg=f"missing variable {key}")
+                value = cdf[key][0]
+                if isinstance(value, datetime.datetime):
+                    continue
+                arr = np.asarray(value)
+                if np.issubdtype(arr.dtype, np.floating):
+                    self.assertTrue(
+                        np.all(np.isfinite(arr)),
+                        msg=f"{key} contains non-finite values: {arr}",
+                    )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/integration/test_swe_processor_integration.py b/tests/integration/test_swe_processor_integration.py
index 82e266444..bc5a9e87e 100644
--- a/tests/integration/test_swe_processor_integration.py
+++ b/tests/integration/test_swe_processor_integration.py
@@ -1,6 +1,8 @@
 import os
+import shutil
 import subprocess
 import sys
+import tempfile
 import unittest
 from pathlib import Path
 from unittest import skipUnless
@@ -14,7 +16,6 @@
 from tests.integration.integration_test_helpers import mock_imap_data_access
 from tests.test_helpers import get_run_local_data_path
 
-
 class SweProcessorIntegration(unittest.TestCase):
     @skipUnless(os.environ.get("IMAP_API_KEY"), "requires production API key")
     def test_swe_processor_with_production_data(self):
@@ -58,30 +59,34 @@ def test_swe_processor_with_local_data(self):
         if expected_file_path.parent.exists():
             expected_file_path.unlink(missing_ok=True)
 
-        input_files = []
-        for file in Path("tests/integration/test_data/swe").iterdir():
-            if file.is_file():
-                input_files.append(file)
-        os.environ["IMAP_DATA_DIR"] = str(OUTPUT_DATA_DIR)
-        with mock_imap_data_access(OUTPUT_DATA_DIR, input_files):
-            result = subprocess.run(
-                [
-                    sys.executable,
-                    "imap_l3_data_processor.py",
-                    "--instrument",
-                    "swe",
-                    "--data-level",
-                    "l3",
-                    "--descriptor",
-                    "sci",
-                    "--start-date",
-                    "20260120",
-                    "--version",
-                    "v001",
-                    "--dependency",
-                    "imap_swe_l3_sci-e979d33c_20260120_v001.json",
-                ],
-            )
+        with tempfile.TemporaryDirectory() as staged_inputs_dir:
+            input_files = []
+            for file in Path("tests/integration/test_data/swe").iterdir():
+                if file.is_file():
+                    staged = Path(staged_inputs_dir, file.name)
+                    shutil.copyfile(file, staged)
+                    input_files.append(staged)
+            os.environ["IMAP_DATA_DIR"] = str(OUTPUT_DATA_DIR)
+            with mock_imap_data_access(OUTPUT_DATA_DIR, input_files):
+                result = subprocess.run(
+                    [
+                        sys.executable,
+                        "imap_l3_data_processor.py",
+                        "--instrument",
+                        "swe",
+                        "--data-level",
+                        "l3",
+                        "--descriptor",
+                        "sci",
+                        "--start-date",
+                        "20260120",
+                        "--version",
+                        "v001",
+                        "--dependency",
+                        "imap_swe_l3_sci-e979d33c_20260120_v001.json",
+                    ],
+                )
+
+                self.assertEqual(0, result.returncode)
+                self.assertTrue(expected_file_path.exists())
 
-            self.assertEqual(0, result.returncode)
-            self.assertTrue(expected_file_path.exists())
diff --git a/tests/swapi/_helpers.py b/tests/swapi/_helpers.py
new file mode 100644
index 000000000..098a80a5b
--- /dev/null
+++ b/tests/swapi/_helpers.py
@@ -0,0 +1,88 @@
+from typing import Optional
+
+import numpy as np
+
+from imap_l3_processing.constants import PROTON_MASS_KG
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    model_solar_wind_ideal_coincidence_rates,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
+from imap_l3_processing.swapi.response.deadtime import deadtime_factor
+from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
+from tests.test_helpers import get_test_instrument_team_data_path
+
+
+# Calibration CSVs shipped with the repo. Loading the full SwapiResponse from
+# these triggers the same code path the production pipeline uses.
+SWAPI_AZIMUTHAL_TRANSMISSION_PATH = get_test_instrument_team_data_path(
+    "swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv"
+)
+SWAPI_CENTRAL_EFFECTIVE_AREA_PATH = get_test_instrument_team_data_path(
+    "swapi/imap_swapi_central-effective-area_20260425_v001.csv"
+)
+SWAPI_PASSBAND_FIT_COEFFICIENTS_PATH = get_test_instrument_team_data_path(
+    "swapi/imap_swapi_passband-fit-coefficients_20260425_v001.csv"
+)
+
+
+def load_swapi_response(
+    warm_cache_voltages: Optional[np.ndarray] = None,
+) -> SwapiResponse:
+    """Build a `SwapiResponse` from the CSV files.."""
+    response = SwapiResponse.from_files(
+        SWAPI_AZIMUTHAL_TRANSMISSION_PATH,
+        SWAPI_CENTRAL_EFFECTIVE_AREA_PATH,
+        SWAPI_PASSBAND_FIT_COEFFICIENTS_PATH,
+    )
+    if warm_cache_voltages is not None:
+        response.warm_cache(warm_cache_voltages)
+    return response
+
+
+# Nominal IMAP_SWAPI → IMAP_RTN rotation at spin phase 0: spin axis (+Y_SWAPI,
+# the boresight) along -R_RTN (SWAPI looks sunward), X_SWAPI along +T_RTN,
+# Z_SWAPI along +N_RTN. Columns are the SWAPI basis vectors expressed in RTN,
+# so `R @ v_swapi = v_rtn` and `R.T @ v_rtn = v_swapi`. The default bulk
+# velocity (+450, 0, 0) km/s arrives along -Y_SWAPI under this rotation.
+# 71-bin realistic SWAPI science sweep (62 coarse bins log-spaced 10000→50 V
+# plus 9 fine bins log-spaced 1000→500 V), matching the production sweep
+# shape used by `SWAPI_COARSE_SWEEP_BINS` and `SWAPI_FINE_SWEEP_BINS`.
+# Caution: The fine sweeps in real data typically start near the peak,
+# this will only start near the peak if the peak is near 1 keV
+REALISTIC_ESA_VOLTAGES = np.concatenate(
+    [
+        np.logspace(np.log10(10000.0), np.log10(50.0), 62),
+        np.logspace(np.log10(1000.0), np.log10(500.0), 9),
+    ]
+)
+
+
+NOMINAL_SWAPI_TO_RTN_ROTATION = np.array(
+    [
+        [0.0, -1.0, 0.0],
+        [1.0, 0.0, 0.0],
+        [0.0, 0.0, 1.0],
+    ]
+)
+
+
+def proton_params(
+    *,
+    density: float = 5.0,
+    velocity_rtn=(450.0, 0, 0.0),
+    temperature: float = 100_000.0,
+) -> SolarWindParams:
+    """Solar-wind proton `SolarWindParams` at typical slow-wind values."""
+    return SolarWindParams(
+        density=density,
+        bulk_velocity_rtn=np.asarray(velocity_rtn, dtype=float),
+        temperature=temperature,
+        mass=PROTON_MASS_KG,
+    )
+
+
+def synthesize_count_rates(ctx, sw_params: SolarWindParams) -> np.ndarray:
+    """Forward-model deadtime-applied coincidence count rates from `sw_params`
+    using the same model the fitter inverts. No Poisson noise."""
+    ideal, _ = model_solar_wind_ideal_coincidence_rates(sw_params, ctx)
+    return ideal * deadtime_factor(ideal)
diff --git a/tests/swapi/l3a/science/solar_wind/__init__.py b/tests/swapi/l3a/science/solar_wind/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/swapi/l3a/science/solar_wind/alpha/__init__.py b/tests/swapi/l3a/science/solar_wind/alpha/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/swapi/l3a/science/solar_wind/alpha/test_calculate_alpha_solar_wind_moments.py b/tests/swapi/l3a/science/solar_wind/alpha/test_calculate_alpha_solar_wind_moments.py
new file mode 100644
index 000000000..3e3876131
--- /dev/null
+++ b/tests/swapi/l3a/science/solar_wind/alpha/test_calculate_alpha_solar_wind_moments.py
@@ -0,0 +1,1075 @@
+import unittest
+from unittest.mock import MagicMock, patch
+
+import numba
+import numpy as np
+from uncertainties import ufloat
+
+from imap_l3_processing.constants import (
+    ALPHA_MASS_PER_CHARGE_M_P_PER_E,
+    ALPHA_PARTICLE_CHARGE_COULOMBS,
+    ALPHA_PARTICLE_MASS_KG,
+    PROTON_CHARGE_COULOMBS,
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.alpha import (
+    calculate_alpha_solar_wind_moments as alpha_module,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.alpha.calculate_alpha_solar_wind_moments import (
+    AlphaSolarWindMoments,
+    _AlphaEvaluator,
+    fit_solar_wind_alpha_moments,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    SolarWindFitContext,
+    build_solar_wind_fit_context,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    model_solar_wind_ideal_coincidence_rates,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_model import (
+    ProtonSolarWindFitResult,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
+from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
+from imap_l3_processing.swapi.response.deadtime import deadtime_factor
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
+from tests.swapi._helpers import load_swapi_response
+
+
+# ----- module-level fixture constants --------------------------------------
+
+# 5-sweep coarse-only voltage axis: 62 bins/sweep × 5 sweeps = 310 measurements,
+# matching the Stage-2 axis described in the doc. The voltage values themselves
+# are arbitrary log-spaced decreasing — `get_alpha_peak_indices` requires
+# strictly decreasing energies, but the exact endpoints are not load-bearing
+# here. The L2 voltage range divided by `SWAPI_L2_K_FACTOR` would give the
+# physical voltages SWAPI sweeps in flight.
+_N_BINS_PER_SWEEP = 62
+_N_SWEEPS = 5
+_ONE_SWEEP_VOLTAGE = np.logspace(
+    np.log10(3500.0), np.log10(140.0), _N_BINS_PER_SWEEP
+)
+_FIVE_SWEEP_VOLTAGE = np.broadcast_to(_ONE_SWEEP_VOLTAGE, (_N_SWEEPS, _N_BINS_PER_SWEEP)).copy()
+_N_MEAS = _FIVE_SWEEP_VOLTAGE.size
+
+# Slow-wind ground-truth moments. RTN +R points sunward, so a sunward solar
+# wind has v_R = -450 km/s. B̂ is taken along -R̂ so a positive Δv pushes
+# alphas toward more-negative v_R (along +B̂).
+_TRUE_PROTON_DENSITY_CM3 = 5.0
+_TRUE_PROTON_TEMPERATURE_K = 1.0e5
+_TRUE_PROTON_VELOCITY_RTN = np.array([-450.0, 0.0, 0.0])
+_TRUE_ALPHA_DENSITY_CM3 = 0.2
+_TRUE_ALPHA_TEMPERATURE_K = 4.0e5
+_TRUE_DELTA_V_KM_S = 30.0
+_B_HAT_RTN = np.array([-1.0, 0.0, 0.0])
+
+# Stage-1 proton uncertainties used to seed `ProtonSolarWindFitResult`.
+# Values are chosen small but nonzero so `bulk_velocity_rtn_covariance`
+# (used downstream by `fit_solar_wind_alpha_moments` to add
+# `σ_Δv²·B̂B̂ᵀ`) is well-defined. The exact values are not pinned by any
+# test — they only need to be finite and positive.
+_STAGE1_PROTON_DENSITY_SIGMA_CM3 = 0.05
+_STAGE1_PROTON_TEMPERATURE_SIGMA_K = 1.0e3
+_STAGE1_PROTON_VELOCITY_SIGMA_KM_S = 1.0
+
+# Predicted ESA voltage of the alpha bump on the fixture spectrum. SWAPI's
+# central-speed conversion is `v² = 2·k·V·(e/m_p) / (m/q in m_p/e)`, so
+# inverting for V at the alpha truth speed (|v_p| + Δv along -R̂, ≈ 480 km/s)
+# gives V_α ≈ 1264 V on this fixture.
+_ALPHA_TRUTH_SPEED_M_S = (
+    float(np.linalg.norm(_TRUE_PROTON_VELOCITY_RTN)) + _TRUE_DELTA_V_KM_S
+) * 1.0e3
+_ALPHA_PEAK_VOLTAGE = (
+    _ALPHA_TRUTH_SPEED_M_S**2
+    * ALPHA_MASS_PER_CHARGE_M_P_PER_E
+    / (2.0 * SWAPI_K_FACTOR * (PROTON_CHARGE_COULOMBS / PROTON_MASS_KG))
+)
+
+
+# ----- helpers --------------------------------------------------------------
+
+
+def _load_swapi_response_with_warm_cache() -> SwapiResponse:
+    """Load `SwapiResponse` from the shipped instrument-team CSVs and warm
+    its passband cache for the fixture voltages.
+
+    `create_response_grid` raises if the passband cache isn't warm for the
+    requested voltage, so callers building a fit context off this response
+    must warm it first."""
+    return load_swapi_response(warm_cache_voltages=_FIVE_SWEEP_VOLTAGE)
+
+
+def _identity_rotation_matrices(n: int = _N_MEAS) -> np.ndarray:
+    """Identity SWAPI→RTN rotations so the instrument frame coincides with
+    RTN — keeps the wind-direction interpretation straightforward.
+
+    `np.broadcast_to` produces a read-only view; `.copy()` materialises a
+    contiguous writeable array, which the JIT'd Stage-2 forward model
+    expects."""
+    return np.broadcast_to(np.eye(3), (n, 3, 3)).copy()
+
+
+def _build_proton_fit_result(
+    *,
+    density: float = _TRUE_PROTON_DENSITY_CM3,
+    temperature: float = _TRUE_PROTON_TEMPERATURE_K,
+    velocity_rtn=_TRUE_PROTON_VELOCITY_RTN,
+    bad_fit_flag: int = int(SwapiL3Flags.NONE),
+) -> ProtonSolarWindFitResult:
+    """Build a `ProtonSolarWindFitResult` (Stage-1 output) with small but
+    nonzero uncertainties so `bulk_velocity_rtn_covariance` is well-defined
+    and Stage-2 has something to add `σ_Δv²·B̂B̂ᵀ` to."""
+    return ProtonSolarWindFitResult(
+        density=ufloat(density, _STAGE1_PROTON_DENSITY_SIGMA_CM3),
+        temperature=ufloat(temperature, _STAGE1_PROTON_TEMPERATURE_SIGMA_K),
+        bulk_velocity_rtn=(
+            ufloat(velocity_rtn[0], _STAGE1_PROTON_VELOCITY_SIGMA_KM_S),
+            ufloat(velocity_rtn[1], _STAGE1_PROTON_VELOCITY_SIGMA_KM_S),
+            ufloat(velocity_rtn[2], _STAGE1_PROTON_VELOCITY_SIGMA_KM_S),
+        ),
+        bad_fit_flag=int(bad_fit_flag),
+    )
+
+
+def _assert_moments_are_nan_filled(test, result) -> None:
+    """Assert that every moment field on an `AlphaSolarWindMoments`
+    fill-valued result is NaN. Used by the two pre-fit guard branches
+    (Stage-1 failed, MAG B̂ NaN) which both short-circuit to fill values."""
+    test.assertTrue(np.isnan(result.density.nominal_value))
+    test.assertTrue(np.isnan(result.temperature.nominal_value))
+    test.assertTrue(np.isnan(result.delta_v.nominal_value))
+    for component in result.bulk_velocity_rtn:
+        test.assertTrue(np.isnan(component.nominal_value))
+
+
+def _synthesize_proton_plus_alpha_count_rate(
+    *,
+    response: SwapiResponse,
+    voltage: np.ndarray,
+    rotation_matrices: np.ndarray,
+    proton_density: float = _TRUE_PROTON_DENSITY_CM3,
+    proton_temperature: float = _TRUE_PROTON_TEMPERATURE_K,
+    proton_velocity_rtn: np.ndarray = _TRUE_PROTON_VELOCITY_RTN,
+    alpha_density: float = _TRUE_ALPHA_DENSITY_CM3,
+    alpha_temperature: float = _TRUE_ALPHA_TEMPERATURE_K,
+    delta_v: float = _TRUE_DELTA_V_KM_S,
+    b_hat: np.ndarray = _B_HAT_RTN,
+):
+    """Forward-model deadtime-applied count rates for a (proton + alpha)
+    spectrum on the fixture voltage axis. Pass `alpha_density=0.0` for a
+    proton-only spectrum.
+
+    Returns the observed count rate, the (pre-deadtime) proton-only true
+    rate, and the alpha velocity v_α = v_p + Δv·B̂. The proton true rate is
+    the same Stage-2 calls "frozen proton model rate" — useful for
+    initial-guess setup as well."""
+    proton_params = SolarWindParams(
+        density=proton_density,
+        bulk_velocity_rtn=np.asarray(proton_velocity_rtn, dtype=float),
+        temperature=proton_temperature,
+        mass=PROTON_MASS_KG,
+    )
+    alpha_velocity = np.asarray(proton_velocity_rtn, dtype=float) + delta_v * b_hat
+    alpha_params = SolarWindParams(
+        density=alpha_density,
+        bulk_velocity_rtn=alpha_velocity,
+        temperature=alpha_temperature,
+        mass=ALPHA_PARTICLE_MASS_KG,
+    )
+    proton_ctx = build_solar_wind_fit_context(
+        count_rate=np.zeros(voltage.shape),
+        esa_voltage=voltage,
+        swapi_response=response,
+        central_effective_area_scale=1.0,
+        rotation_matrices=rotation_matrices,
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    )
+    proton_true, _ = model_solar_wind_ideal_coincidence_rates(proton_params, proton_ctx)
+    if alpha_density > 0.0:
+        alpha_ctx = build_solar_wind_fit_context(
+            count_rate=np.zeros(voltage.shape),
+            esa_voltage=voltage,
+            swapi_response=response,
+            central_effective_area_scale=1.0,
+            rotation_matrices=rotation_matrices,
+            mass_kg=ALPHA_PARTICLE_MASS_KG,
+            mass_per_charge_m_p_per_e=ALPHA_MASS_PER_CHARGE_M_P_PER_E,
+        )
+        alpha_true, _ = model_solar_wind_ideal_coincidence_rates(alpha_params, alpha_ctx)
+        total_true = proton_true + alpha_true
+    else:
+        total_true = proton_true
+    observed = (total_true * deadtime_factor(total_true)).reshape(voltage.shape)
+    return observed, proton_true, alpha_velocity
+
+
+class _SyntheticAlphaSpectrumFixture:
+    """Cached forward-model fixture: builds the SwapiResponse, warm-caches
+    its passband, and synthesizes a (proton + alpha) count-rate spectrum
+    once for all subclass tests. Class-level so JIT compile + the pandas
+    pivot inside `_build_passband_array` are paid one time."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.response = _load_swapi_response_with_warm_cache()
+        cls.voltage = _FIVE_SWEEP_VOLTAGE
+        cls.rotation_matrices = _identity_rotation_matrices()
+        observed, proton_true, alpha_v = _synthesize_proton_plus_alpha_count_rate(
+            response=cls.response,
+            voltage=cls.voltage,
+            rotation_matrices=cls.rotation_matrices,
+        )
+        cls.observed_count_rate = observed
+        cls.proton_true_rate = proton_true
+        cls.alpha_velocity_rtn = alpha_v
+
+
+# ----- fit_solar_wind_alpha_moments — guard conditions ---------------------
+
+
+class TestFitAlphaMomentsGuardBranches(unittest.TestCase):
+    """Tests for `fit_solar_wind_alpha_moments` — pre-fit guard branches (proton fill values, missing/non-finite MAG B̂) must short-circuit to a NaN-filled moments result with the proton's flag propagated before any forward-model evaluation."""
+
+    def test_proton_fill_values_propagate_proton_flag_to_alpha(self):
+        """When the Stage-1 proton fit returned NaN moments (fill values), the alpha result inherits the proton's `bad_fit_flag` unchanged — no separate alpha-side flag is added for "stage 1 failed"."""
+        proton_moments = _build_proton_fit_result(
+            velocity_rtn=np.array([np.nan, np.nan, np.nan]),
+            bad_fit_flag=int(SwapiL3Flags.FIT_ERROR),
+        )
+        result = fit_solar_wind_alpha_moments(
+            count_rate=np.zeros(_FIVE_SWEEP_VOLTAGE.shape),
+            esa_voltage=_FIVE_SWEEP_VOLTAGE,
+            measurement_time=np.zeros(_N_MEAS),
+            # `swapi_response` should not be touched on this branch — but
+            # pass a real one so an unexpected dereference would not crash.
+            swapi_response=_load_swapi_response_with_warm_cache(),
+            proton_moments=proton_moments,
+            magnetic_field_direction=_B_HAT_RTN,
+            alpha_effective_area_scale=1.0,
+            proton_effective_area_scale=1.0,
+            rotation_matrices=_identity_rotation_matrices(),
+        )
+        self.assertEqual(result.bad_fit_flag, int(SwapiL3Flags.FIT_ERROR))
+
+    def test_proton_fill_values_return_nan_filled_alpha_moments(self):
+        """When the Stage-1 proton fit returned NaN moments, every alpha moment field is filled with NaN so downstream consumers can distinguish "no fit attempted" from "fit succeeded with degenerate values"."""
+        proton_moments = _build_proton_fit_result(
+            velocity_rtn=np.array([np.nan, np.nan, np.nan]),
+            bad_fit_flag=int(SwapiL3Flags.FIT_ERROR),
+        )
+        result = fit_solar_wind_alpha_moments(
+            count_rate=np.zeros(_FIVE_SWEEP_VOLTAGE.shape),
+            esa_voltage=_FIVE_SWEEP_VOLTAGE,
+            measurement_time=np.zeros(_N_MEAS),
+            swapi_response=_load_swapi_response_with_warm_cache(),
+            proton_moments=proton_moments,
+            magnetic_field_direction=_B_HAT_RTN,
+            alpha_effective_area_scale=1.0,
+            proton_effective_area_scale=1.0,
+            rotation_matrices=_identity_rotation_matrices(),
+        )
+        _assert_moments_are_nan_filled(self, result)
+
+    def test_mag_gap_propagates_proton_flag_with_no_dedicated_bit(self):
+        """A NaN component in `magnetic_field_direction` is treated as an ordinary data gap: the fitter short-circuits and propagates the proton's flag unchanged with no dedicated MAG-gap bit added."""
+        proton_moments = _build_proton_fit_result()
+        nan_b_hat = np.array([np.nan, 0.0, 0.0])
+        result = fit_solar_wind_alpha_moments(
+            count_rate=np.zeros(_FIVE_SWEEP_VOLTAGE.shape),
+            esa_voltage=_FIVE_SWEEP_VOLTAGE,
+            measurement_time=np.zeros(_N_MEAS),
+            swapi_response=_load_swapi_response_with_warm_cache(),
+            proton_moments=proton_moments,
+            magnetic_field_direction=nan_b_hat,
+            alpha_effective_area_scale=1.0,
+            proton_effective_area_scale=1.0,
+            rotation_matrices=_identity_rotation_matrices(),
+        )
+        self.assertEqual(result.bad_fit_flag, int(SwapiL3Flags.NONE))
+
+    def test_nan_magnetic_field_direction_returns_nan_filled_moments(self):
+        """A NaN B̂ short-circuits before any forward-model call and every moment field is filled with NaN, mirroring the Stage-1-failure guard."""
+        proton_moments = _build_proton_fit_result()
+        nan_b_hat = np.array([np.nan, 0.0, 0.0])
+        result = fit_solar_wind_alpha_moments(
+            count_rate=np.zeros(_FIVE_SWEEP_VOLTAGE.shape),
+            esa_voltage=_FIVE_SWEEP_VOLTAGE,
+            measurement_time=np.zeros(_N_MEAS),
+            swapi_response=_load_swapi_response_with_warm_cache(),
+            proton_moments=proton_moments,
+            magnetic_field_direction=nan_b_hat,
+            alpha_effective_area_scale=1.0,
+            proton_effective_area_scale=1.0,
+            rotation_matrices=_identity_rotation_matrices(),
+        )
+        _assert_moments_are_nan_filled(self, result)
+
+
+# ----- fit_solar_wind_alpha_moments — context construction -----------------
+
+
+class TestFitAlphaMomentsContextConstruction(unittest.TestCase):
+    """Tests for `fit_solar_wind_alpha_moments` — verifies it builds two separate fit contexts (proton background and alpha bump) with the correct species mass / mass-per-charge / effective-area scale routed to each."""
+
+    def setUp(self):
+        self.proton_moments = _build_proton_fit_result()
+        self.swapi_response = _load_swapi_response_with_warm_cache()
+        self.proton_eff_scale = 0.987
+        self.alpha_eff_scale = 0.731
+        self.rotation_matrices = _identity_rotation_matrices()
+
+        build_ctx_patcher = patch(
+            "imap_l3_processing.swapi.l3a.science.solar_wind.alpha."
+            "calculate_alpha_solar_wind_moments.build_solar_wind_fit_context"
+        )
+        initial_guess_patcher = patch(
+            "imap_l3_processing.swapi.l3a.science.solar_wind.alpha."
+            "calculate_alpha_solar_wind_moments._alpha_initial_guess",
+            return_value=None,
+        )
+        forward_model_patcher = patch(
+            "imap_l3_processing.swapi.l3a.science.solar_wind.alpha."
+            "calculate_alpha_solar_wind_moments.model_solar_wind_ideal_coincidence_rates",
+            return_value=(np.zeros(_N_MEAS), np.zeros((_N_MEAS, 5))),
+        )
+        self.addCleanup(build_ctx_patcher.stop)
+        self.addCleanup(initial_guess_patcher.stop)
+        self.addCleanup(forward_model_patcher.stop)
+        self.mock_build_ctx = build_ctx_patcher.start()
+        initial_guess_patcher.start()
+        forward_model_patcher.start()
+        self.mock_build_ctx.return_value = MagicMock()
+
+        fit_solar_wind_alpha_moments(
+            count_rate=np.zeros(_FIVE_SWEEP_VOLTAGE.shape),
+            esa_voltage=_FIVE_SWEEP_VOLTAGE,
+            measurement_time=np.zeros(_N_MEAS),
+            swapi_response=self.swapi_response,
+            proton_moments=self.proton_moments,
+            magnetic_field_direction=_B_HAT_RTN,
+            alpha_effective_area_scale=self.alpha_eff_scale,
+            proton_effective_area_scale=self.proton_eff_scale,
+            rotation_matrices=self.rotation_matrices,
+        )
+        self.proton_call_kwargs = self.mock_build_ctx.call_args_list[0].kwargs
+        self.alpha_call_kwargs = self.mock_build_ctx.call_args_list[1].kwargs
+
+    def test_two_contexts_are_built(self):
+        """The fitter constructs exactly one context for the frozen proton background and one for the alpha bump."""
+        self.assertEqual(self.mock_build_ctx.call_count, 2)
+
+    def test_proton_context_uses_proton_mass_per_charge(self):
+        """The proton-background context receives `PROTON_MASS_PER_CHARGE_M_P_PER_E`, so its speed→voltage mapping matches the proton species."""
+        self.assertEqual(
+            self.proton_call_kwargs["mass_per_charge_m_p_per_e"],
+            PROTON_MASS_PER_CHARGE_M_P_PER_E,
+        )
+
+    def test_alpha_context_uses_alpha_mass_per_charge(self):
+        """The alpha-bump context receives `ALPHA_MASS_PER_CHARGE_M_P_PER_E` (≈2), guarding against a regression that reuses the proton 1.0."""
+        self.assertEqual(
+            self.alpha_call_kwargs["mass_per_charge_m_p_per_e"],
+            ALPHA_MASS_PER_CHARGE_M_P_PER_E,
+        )
+
+    def test_proton_context_uses_proton_mass_kg(self):
+        """The proton-background context receives `PROTON_MASS_KG` so the Maxwellian thermal-speed scale matches the proton species."""
+        self.assertEqual(self.proton_call_kwargs["mass_kg"], PROTON_MASS_KG)
+
+    def test_alpha_context_uses_alpha_mass_kg(self):
+        """The alpha-bump context receives `ALPHA_PARTICLE_MASS_KG` so the Maxwellian thermal-speed scale matches the alpha species."""
+        self.assertEqual(self.alpha_call_kwargs["mass_kg"], ALPHA_PARTICLE_MASS_KG)
+
+    def test_proton_context_uses_proton_effective_area_scale(self):
+        """The proton-background context receives the caller-supplied proton EA scale on `central_effective_area_scale`."""
+        self.assertEqual(
+            self.proton_call_kwargs["central_effective_area_scale"],
+            self.proton_eff_scale,
+        )
+
+    def test_alpha_context_uses_alpha_effective_area_scale(self):
+        """The alpha-bump context receives the caller-supplied alpha EA scale (which folds A_α/A_p species correction and alpha time drift into one ratio) on `central_effective_area_scale`."""
+        self.assertEqual(
+            self.alpha_call_kwargs["central_effective_area_scale"],
+            self.alpha_eff_scale,
+        )
+
+    def test_alpha_and_proton_effective_area_scales_are_distinct_values(self):
+        """The two species use different EA scales — guards against a regression that wires both contexts to the same scalar."""
+        self.assertNotEqual(
+            self.alpha_call_kwargs["central_effective_area_scale"],
+            self.proton_call_kwargs["central_effective_area_scale"],
+        )
+
+
+# ----- fit_solar_wind_alpha_moments — end-to-end recovery -----------------
+
+
+class TestFitAlphaMomentsRecoversTruth(
+    _SyntheticAlphaSpectrumFixture, unittest.TestCase
+):
+    """Tests for `fit_solar_wind_alpha_moments` — end-to-end Stage-2 recovery: synthesize a proton+alpha spectrum from known truth, hand the fitter the exact proton moments, and verify it recovers (n_α, T_α, Δv) and the field-aligned alpha velocity."""
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.proton_moments = _build_proton_fit_result()
+        cls.result = fit_solar_wind_alpha_moments(
+            count_rate=cls.observed_count_rate,
+            esa_voltage=cls.voltage,
+            measurement_time=np.zeros(_N_MEAS),
+            swapi_response=cls.response,
+            proton_moments=cls.proton_moments,
+            magnetic_field_direction=_B_HAT_RTN,
+            alpha_effective_area_scale=1.0,
+            proton_effective_area_scale=1.0,
+            rotation_matrices=cls.rotation_matrices,
+        )
+
+    def test_fit_succeeds_with_no_quality_flags(self):
+        """A clean synthetic spectrum + exact proton moments must yield `bad_fit_flag == NONE` — no LM convergence issues, no missing inputs."""
+        self.assertEqual(self.result.bad_fit_flag, int(SwapiL3Flags.NONE))
+
+    def test_recovers_alpha_density(self):
+        """The fitted alpha density matches the synthesis truth to ~1% (LM termination + deadtime rounding)."""
+        np.testing.assert_allclose(
+            self.result.density.nominal_value,
+            _TRUE_ALPHA_DENSITY_CM3,
+            rtol=1e-2,
+        )
+
+    def test_recovers_alpha_temperature(self):
+        """The fitted alpha temperature matches the synthesis truth to ~1%."""
+        np.testing.assert_allclose(
+            self.result.temperature.nominal_value,
+            _TRUE_ALPHA_TEMPERATURE_K,
+            rtol=1e-2,
+        )
+
+    def test_recovers_signed_delta_v(self):
+        """The fitted Δv matches the truth in magnitude and sign — LM recovers the correct basin from the Δv=0 seed."""
+        np.testing.assert_allclose(
+            self.result.delta_v.nominal_value, _TRUE_DELTA_V_KM_S, atol=0.5
+        )
+
+    def test_alpha_velocity_equals_proton_velocity_plus_delta_v_along_bhat(self):
+        """The post-fit `bulk_velocity_rtn` satisfies the algebraic identity v_α = v_p* + Δv·B̂ exactly (not approximately) — the dataclass stores the constraint, not a free vector."""
+        v_alpha = self.result.bulk_velocity_rtn_nominal()
+        expected = (
+            _TRUE_PROTON_VELOCITY_RTN
+            + self.result.delta_v.nominal_value * _B_HAT_RTN
+        )
+        np.testing.assert_allclose(v_alpha, expected, atol=1e-9)
+
+    def test_alpha_velocity_recovers_truth(self):
+        """The recovered alpha velocity vector matches the synthesis truth (≈480 km/s) within ~1 km/s — combines Δv recovery + field-aligned constraint."""
+        np.testing.assert_allclose(
+            self.result.bulk_velocity_rtn_nominal(),
+            self.alpha_velocity_rtn,
+            atol=1.0,
+        )
+
+
+class TestFitAlphaMomentsAlphaVelocityFollowsBHat(unittest.TestCase):
+    """Tests for `fit_solar_wind_alpha_moments` — the field-aligned drift constraint v_α = v_p + Δv·B̂ must hold for any B̂ direction, including B̂ not parallel to -R̂."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.response = _load_swapi_response_with_warm_cache()
+        cls.rotation_matrices = _identity_rotation_matrices()
+        cls.proton_velocity_rtn = _TRUE_PROTON_VELOCITY_RTN
+        # B̂ tilted away from -R̂ by `tilt_deg` in the R-T plane.
+        tilt_deg = 10.0
+        tilt_rad = np.deg2rad(tilt_deg)
+        cls.b_hat = np.array([-np.cos(tilt_rad), np.sin(tilt_rad), 0.0])
+        observed, _proton_true, alpha_v = _synthesize_proton_plus_alpha_count_rate(
+            response=cls.response,
+            voltage=_FIVE_SWEEP_VOLTAGE,
+            rotation_matrices=cls.rotation_matrices,
+            proton_velocity_rtn=cls.proton_velocity_rtn,
+            delta_v=_TRUE_DELTA_V_KM_S,
+            b_hat=cls.b_hat,
+        )
+        cls.alpha_velocity_truth = alpha_v
+        cls.proton_moments = _build_proton_fit_result(
+            velocity_rtn=cls.proton_velocity_rtn
+        )
+        cls.result = fit_solar_wind_alpha_moments(
+            count_rate=observed,
+            esa_voltage=_FIVE_SWEEP_VOLTAGE,
+            measurement_time=np.zeros(_N_MEAS),
+            swapi_response=cls.response,
+            proton_moments=cls.proton_moments,
+            magnetic_field_direction=cls.b_hat,
+            alpha_effective_area_scale=1.0,
+            proton_effective_area_scale=1.0,
+            rotation_matrices=cls.rotation_matrices,
+        )
+
+    def test_alpha_velocity_minus_proton_velocity_is_parallel_to_bhat(self):
+        """For a tilted B̂, the recovered (v_α − v_p) lies along ±B̂ — equivalently, the cross product (v_α − v_p) × B̂ is zero up to numerical noise."""
+        delta = (
+            self.result.bulk_velocity_rtn_nominal() - self.proton_velocity_rtn
+        )
+        np.testing.assert_allclose(np.cross(delta, self.b_hat), 0.0, atol=1e-9)
+
+    def test_recovered_delta_v_matches_dot_product_of_velocity_offset(self):
+        """The stored `delta_v` equals (v_α − v_p)·B̂ since B̂ is unit-normed — the scalar matches the projection of the vector offset onto the field."""
+        delta_along = float(
+            np.dot(
+                self.result.bulk_velocity_rtn_nominal() - self.proton_velocity_rtn,
+                self.b_hat,
+            )
+        )
+        np.testing.assert_allclose(
+            self.result.delta_v.nominal_value, delta_along, atol=1e-9
+        )
+
+    def test_alpha_velocity_recovers_truth_under_tilted_bhat(self):
+        """With a 10° tilted B̂, the recovered alpha velocity matches the synthesis truth vector to ~1 km/s."""
+        np.testing.assert_allclose(
+            self.result.bulk_velocity_rtn_nominal(),
+            self.alpha_velocity_truth,
+            atol=1.0,
+        )
+
+
+# ----- AlphaSolarWindMoments accessors --------------------------------------
+
+
+class TestAlphaSolarWindMomentsAccessors(unittest.TestCase):
+    """Tests for `AlphaSolarWindMoments.bulk_velocity_rtn_nominal` and `bulk_velocity_rtn_covariance` — accessors that extract the nominal vector and covariance matrix from the correlated UFloat triple."""
+
+    def setUp(self):
+        # Build moments from a known correlated velocity covariance so the
+        # accessor outputs are predictable. Using `make_correlated_velocity`
+        # would require reconstructing the same covariance the alpha
+        # pipeline produces; building the UFloat triple directly is enough
+        # to exercise the accessors.
+        from imap_l3_processing.swapi.l3a.science.solar_wind.uncertainties import (
+            make_correlated_velocity,
+        )
+
+        self.expected_nominal = np.array([-450.0, 10.0, -5.0])
+        # Symmetric, positive-definite — `correlated_values` requires PSD.
+        self.expected_covariance = np.array(
+            [
+                [4.0, 1.0, 0.5],
+                [1.0, 9.0, 0.25],
+                [0.5, 0.25, 16.0],
+            ]
+        )
+        velocity_triple = make_correlated_velocity(
+            self.expected_nominal, self.expected_covariance
+        )
+        self.moments = AlphaSolarWindMoments(
+            density=ufloat(0.2, 0.01),
+            temperature=ufloat(4.0e5, 1.0e3),
+            bulk_velocity_rtn=velocity_triple,
+            delta_v=ufloat(30.0, 1.0),
+            bad_fit_flag=int(SwapiL3Flags.NONE),
+        )
+
+    def test_bulk_velocity_rtn_nominal_returns_per_component_nominals(self):
+        """The nominal-vector accessor returns the per-component nominal_values as a length-3 ndarray."""
+        np.testing.assert_array_equal(
+            self.moments.bulk_velocity_rtn_nominal(), self.expected_nominal
+        )
+
+    def test_bulk_velocity_rtn_covariance_is_three_by_three(self):
+        """The covariance accessor returns a 3×3 matrix matching the RTN velocity dimensionality."""
+        covariance = self.moments.bulk_velocity_rtn_covariance()
+        self.assertEqual(covariance.shape, (3, 3))
+
+    def test_bulk_velocity_rtn_covariance_matches_input_covariance(self):
+        """The covariance accessor round-trips: a UFloat triple built from a known PSD covariance returns that same matrix to machine precision."""
+        np.testing.assert_allclose(
+            self.moments.bulk_velocity_rtn_covariance(),
+            self.expected_covariance,
+            atol=1e-12,
+        )
+
+
+# ----- fit_solar_wind_alpha_moments — geometry fallback --------------------
+
+
+class TestFitAlphaMomentsGeometryFallback(unittest.TestCase):
+    """Tests for `fit_solar_wind_alpha_moments` — when `rotation_matrices` is omitted, SWAPI→RTN geometry is resolved from `measurement_time` via SPICE (`get_swapi_geometry`); the Stage-1 caller normally precomputes geometry and shares it, but the standalone-callable fallback path must also work."""
+
+    def test_fetches_geometry_from_measurement_time_when_rotation_matrices_omitted(
+        self,
+    ):
+        """Calling the fitter without `rotation_matrices` triggers `get_swapi_geometry(measurement_time)` and reaches the initial-guess step rather than crashing on missing geometry."""
+        proton_moments = _build_proton_fit_result()
+        rotation_matrices_from_geometry = _identity_rotation_matrices()
+        measurement_time = np.arange(_N_MEAS, dtype=float)
+
+        with patch(
+            "imap_l3_processing.swapi.l3a.utils.get_swapi_geometry",
+            return_value=rotation_matrices_from_geometry,
+        ) as mock_get_geometry, patch.object(
+            alpha_module,
+            "_alpha_initial_guess",
+            return_value=None,
+        ):
+            result = fit_solar_wind_alpha_moments(
+                count_rate=np.zeros(_FIVE_SWEEP_VOLTAGE.shape),
+                esa_voltage=_FIVE_SWEEP_VOLTAGE,
+                measurement_time=measurement_time,
+                swapi_response=_load_swapi_response_with_warm_cache(),
+                proton_moments=proton_moments,
+                magnetic_field_direction=_B_HAT_RTN,
+                alpha_effective_area_scale=1.0,
+                proton_effective_area_scale=1.0,
+                rotation_matrices=None,
+            )
+
+        mock_get_geometry.assert_called_once()
+        np.testing.assert_array_equal(
+            mock_get_geometry.call_args.args[0], measurement_time
+        )
+        # The mocked initial guess returned `None`, so the fitter
+        # short-circuited to a FIT_ERROR moments result — proving it
+        # reached the initial-guess step (i.e. did not crash on geometry).
+        self.assertEqual(result.bad_fit_flag, int(SwapiL3Flags.FIT_ERROR))
+
+
+# ----- fit_solar_wind_alpha_moments — peak-bin filtering -------------------
+
+
+class TestFitAlphaMomentsPeakBinFiltering(
+    _SyntheticAlphaSpectrumFixture, unittest.TestCase
+):
+    """Tests for `fit_solar_wind_alpha_moments` — peak-bin subsetting plus zero-count filtering: Stage-2 keeps only the alpha-peak bins across sweeps, then drops any with `count_rate <= 0` so deadtime correction never divides by zero."""
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        # Zero out a single peak-region bin in every sweep — the initial
+        # guess will still find a peak (the rest of the bump is intact), but
+        # the zeroed bins must be dropped from the LM residual axis.
+        from imap_l3_processing.swapi.l3a.science.solar_wind.alpha.calculate_alpha_solar_wind_moments import (
+            _alpha_initial_guess,
+        )
+
+        alpha_ctx_full = build_solar_wind_fit_context(
+            count_rate=cls.observed_count_rate,
+            esa_voltage=cls.voltage,
+            swapi_response=cls.response,
+            central_effective_area_scale=1.0,
+            rotation_matrices=cls.rotation_matrices,
+            mass_kg=ALPHA_PARTICLE_MASS_KG,
+            mass_per_charge_m_p_per_e=ALPHA_MASS_PER_CHARGE_M_P_PER_E,
+        )
+        guess = _alpha_initial_guess(
+            proton_true_rate=cls.proton_true_rate,
+            proton_temperature=_TRUE_PROTON_TEMPERATURE_K,
+            alpha_ctx=alpha_ctx_full,
+            proton_bulk_velocity_rtn=_TRUE_PROTON_VELOCITY_RTN,
+            magnetic_field_direction=_B_HAT_RTN,
+        )
+        _, _, _, peak_bin_idx = guess
+        zeroed_observed = cls.observed_count_rate.copy()
+        target_bin = int(peak_bin_idx[len(peak_bin_idx) // 2])
+        zeroed_observed[:, target_bin] = 0.0
+        cls.zeroed_observed_count_rate = zeroed_observed
+        cls.target_bin = target_bin
+
+        cls.proton_moments = _build_proton_fit_result()
+        cls.result = fit_solar_wind_alpha_moments(
+            count_rate=zeroed_observed,
+            esa_voltage=cls.voltage,
+            measurement_time=np.zeros(_N_MEAS),
+            swapi_response=cls.response,
+            proton_moments=cls.proton_moments,
+            magnetic_field_direction=_B_HAT_RTN,
+            alpha_effective_area_scale=1.0,
+            proton_effective_area_scale=1.0,
+            rotation_matrices=cls.rotation_matrices,
+        )
+
+    def test_fit_completes_when_some_peak_bins_are_zeroed(self):
+        """Zeroing one peak-region bin per sweep does not crash the fitter and still flags NONE — the zeroed bins are filtered out of the LM residual axis before deadtime correction runs on them."""
+        self.assertEqual(self.result.bad_fit_flag, int(SwapiL3Flags.NONE))
+
+    def test_recovers_alpha_density_within_loose_bound_after_zeroing(self):
+        """Density recovery is within ~10% of truth after losing one bin per sweep — the residual peak still carries most of the alpha signal."""
+        np.testing.assert_allclose(
+            self.result.density.nominal_value,
+            _TRUE_ALPHA_DENSITY_CM3,
+            rtol=0.1,
+        )
+
+
+# ----- fit_solar_wind_alpha_moments — non-converged LM ---------------------
+
+
+class TestFitAlphaMomentsLMFailureFlag(unittest.TestCase):
+    """Tests for `fit_solar_wind_alpha_moments` — when LM does not converge (`result.success=False`), the chunk is rejected: every moment is NaN-filled and `FIT_ERROR` is set."""
+
+    def test_fit_error_flag_set_when_least_squares_does_not_converge(self):
+        """A non-converged LM result triggers the fit-quality guard: density and the other moments are NaN-filled and `FIT_ERROR` is reported alone."""
+        proton_moments = _build_proton_fit_result()
+
+        x_fit = np.array([np.log(0.2), np.log(4.0e5), 30.0])
+        residual_norm = np.full(_N_MEAS, 1.0)
+        jac = np.zeros((_N_MEAS, 3))
+        jac[0, 0] = 1.0
+        jac[0, 1] = 1.0
+        jac[0, 2] = 1.0
+        non_converged = MagicMock()
+        non_converged.x = x_fit
+        non_converged.fun = residual_norm
+        non_converged.jac = jac
+        non_converged.success = False
+
+        with patch.object(
+            alpha_module,
+            "_alpha_initial_guess",
+            return_value=(0.2, 4.0e5, 0.0, np.array([10, 11, 12])),
+        ), patch.object(
+            alpha_module._AlphaEvaluator,
+            "residuals",
+            return_value=np.full(_N_MEAS, 1.0),
+        ), patch.object(
+            alpha_module.scipy.optimize,
+            "least_squares",
+            return_value=non_converged,
+        ):
+            result = fit_solar_wind_alpha_moments(
+                count_rate=np.full(_FIVE_SWEEP_VOLTAGE.shape, 100.0),
+                esa_voltage=_FIVE_SWEEP_VOLTAGE,
+                measurement_time=np.zeros(_N_MEAS),
+                swapi_response=_load_swapi_response_with_warm_cache(),
+                proton_moments=proton_moments,
+                magnetic_field_direction=_B_HAT_RTN,
+                alpha_effective_area_scale=1.0,
+                proton_effective_area_scale=1.0,
+                rotation_matrices=_identity_rotation_matrices(),
+            )
+
+        self.assertEqual(result.bad_fit_flag, int(SwapiL3Flags.FIT_ERROR))
+        self.assertTrue(np.isnan(result.density.nominal_value))
+
+
+# ----- fit_solar_wind_alpha_moments — initial-guess failure branches -------
+
+
+class TestFitAlphaMomentsInitialGuessFailures(unittest.TestCase):
+    """Tests for `fit_solar_wind_alpha_moments` — failure branches inside `_alpha_initial_guess` and `_infer_sweep_layout` that return None, causing the public function to short-circuit to a `FIT_ERROR` NaN-filled result."""
+
+    def _call(self, *, count_rate, esa_voltage, rotation_matrices=None):
+        n_meas = esa_voltage.size
+        if rotation_matrices is None:
+            rotation_matrices = np.broadcast_to(np.eye(3), (n_meas, 3, 3)).copy()
+        warm_voltages = esa_voltage if n_meas > 0 else None
+        response = load_swapi_response(warm_cache_voltages=warm_voltages)
+        return fit_solar_wind_alpha_moments(
+            count_rate=count_rate,
+            esa_voltage=esa_voltage,
+            measurement_time=np.zeros(n_meas),
+            swapi_response=response,
+            proton_moments=_build_proton_fit_result(),
+            magnetic_field_direction=_B_HAT_RTN,
+            alpha_effective_area_scale=1.0,
+            proton_effective_area_scale=1.0,
+            rotation_matrices=rotation_matrices,
+        )
+
+    def _assert_fit_error_nan(self, result):
+        self.assertEqual(result.bad_fit_flag, int(SwapiL3Flags.FIT_ERROR))
+        _assert_moments_are_nan_filled(self, result)
+
+    def test_one_dimensional_inputs_short_circuit_to_fit_error(self):
+        """`_alpha_initial_guess` requires a 2D (n_sweeps, n_bins) count_rate; a 1D input falls through the ndim guard and the fitter reports `FIT_ERROR`."""
+        voltage = np.logspace(np.log10(3500.0), np.log10(140.0), _N_MEAS)
+        result = self._call(
+            count_rate=np.zeros(_N_MEAS),
+            esa_voltage=voltage,
+        )
+        self._assert_fit_error_nan(result)
+
+    def test_non_monotonic_voltage_raises_in_peak_finder_and_returns_fit_error(self):
+        """A non-monotonic per-sweep voltage axis violates `get_alpha_peak_indices`'s decreasing-energies assertion; the `try/except Exception` in `_alpha_initial_guess` catches it and the fitter returns `FIT_ERROR`."""
+        one_sweep = _ONE_SWEEP_VOLTAGE.copy()
+        one_sweep[10], one_sweep[11] = one_sweep[11], one_sweep[10]
+        voltage = np.broadcast_to(one_sweep, (_N_SWEEPS, _N_BINS_PER_SWEEP)).copy()
+        result = self._call(
+            count_rate=np.ones(voltage.shape),
+            esa_voltage=voltage,
+        )
+        self._assert_fit_error_nan(result)
+
+    def test_short_peak_window_returns_fit_error(self):
+        """When `get_alpha_peak_indices` returns a slice with fewer than 3 bins, `_alpha_initial_guess` returns `None` and the fitter reports `FIT_ERROR`."""
+        with patch.object(
+            alpha_module,
+            "get_alpha_peak_indices",
+            return_value=slice(10, 12),
+        ):
+            result = self._call(
+                count_rate=np.ones(_FIVE_SWEEP_VOLTAGE.shape),
+                esa_voltage=_FIVE_SWEEP_VOLTAGE,
+            )
+        self._assert_fit_error_nan(result)
+
+    def test_no_positive_residual_at_peak_returns_fit_error(self):
+        """When the residual `count_avg − 2·proton_bg_avg` has no positive entry inside the peak window (e.g. count_rate=0 everywhere), `_alpha_initial_guess` returns `None`."""
+        with patch.object(
+            alpha_module,
+            "get_alpha_peak_indices",
+            return_value=slice(10, 20),
+        ):
+            result = self._call(
+                count_rate=np.zeros(_FIVE_SWEEP_VOLTAGE.shape),
+                esa_voltage=_FIVE_SWEEP_VOLTAGE,
+            )
+        self._assert_fit_error_nan(result)
+
+    def test_non_positive_unit_alpha_denominator_returns_fit_error(self):
+        """When the unit-density alpha forward model evaluates to ~0 at every peak bin (peak slice placed in the highest-voltage bins, far above the alpha resonance), `denom <= 0` and `_alpha_initial_guess` returns `None`."""
+        # Inject a positive residual at the chosen peak bins by overriding
+        # count_rate at those locations, then place the peak slice in the
+        # high-voltage tail where unit_alpha ~ 0 (alpha truth voltage is
+        # ~1264 V, voltage bin 0 is 3500 V).
+        count_rate = np.zeros((_N_SWEEPS, _N_BINS_PER_SWEEP))
+        peak_slice = slice(0, 3)
+        count_rate[:, peak_slice] = 1.0e3
+        with patch.object(
+            alpha_module,
+            "get_alpha_peak_indices",
+            return_value=peak_slice,
+        ):
+            result = self._call(
+                count_rate=count_rate,
+                esa_voltage=_FIVE_SWEEP_VOLTAGE,
+            )
+        self._assert_fit_error_nan(result)
+
+
+# Doc contracts intentionally not pinned here: upstream-set quality flags
+# (`PRELIMINARY_MAG`) belong in chunk-fitter / processor tests, and the
+# LM-jacobian-derived sigmas (`σ_n_α`, `σ_T_α`,
+# `σ_Δv`) plus the `Σ_v_α = Σ_v_p + σ_Δv²·B̂B̂ᵀ` covariance update would
+# require an independent numerical reference to pin without coupling to
+# scipy version drift.
+
+
+# ----- _AlphaEvaluator analytic Jacobian -----------------------------------
+
+
+class TestAlphaEvaluatorAnalyticJacobianMatchesFiniteDifference(
+    _SyntheticAlphaSpectrumFixture, unittest.TestCase
+):
+    """Tests for `_AlphaEvaluator.jacobian` — verifies the analytic 3-column Jacobian (∂residuals/∂[log n_α, log T_α, Δv]) matches a central-difference numerical Jacobian within the same tolerance used for the proton-fit forward-model Jacobian (5% rtol). The small mismatch comes from the dynamic-quadrature limits shifting when the state is perturbed for finite differences, per `docs/swapi/solar-wind-moments.md` § Boundary terms."""
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.alpha_ctx = build_solar_wind_fit_context(
+            count_rate=cls.observed_count_rate,
+            esa_voltage=cls.voltage,
+            swapi_response=cls.response,
+            central_effective_area_scale=1.0,
+            rotation_matrices=cls.rotation_matrices,
+            mass_kg=ALPHA_PARTICLE_MASS_KG,
+            mass_per_charge_m_p_per_e=ALPHA_MASS_PER_CHARGE_M_P_PER_E,
+        )
+        cls.evaluator = _AlphaEvaluator(
+            proton_bulk=_TRUE_PROTON_VELOCITY_RTN,
+            magnetic_field_direction=_B_HAT_RTN,
+            proton_true_rate=cls.proton_true_rate,
+            alpha_ctx=cls.alpha_ctx,
+        )
+        cls.x0 = np.array(
+            [
+                np.log(_TRUE_ALPHA_DENSITY_CM3),
+                np.log(_TRUE_ALPHA_TEMPERATURE_K),
+                _TRUE_DELTA_V_KM_S,
+            ]
+        )
+        cls.analytic_jacobian = cls.evaluator.jacobian(cls.x0).copy()
+        steps = np.array([1.0e-5, 1.0e-5, 1.0e-3])
+        n_residuals = cls.analytic_jacobian.shape[0]
+        cls.numerical_jacobian = np.empty((n_residuals, 3))
+        for j in range(3):
+            x_plus = cls.x0.copy()
+            x_minus = cls.x0.copy()
+            x_plus[j] += steps[j]
+            x_minus[j] -= steps[j]
+            r_plus = cls.evaluator.residuals(x_plus).copy()
+            r_minus = cls.evaluator.residuals(x_minus).copy()
+            cls.numerical_jacobian[:, j] = (r_plus - r_minus) / (2.0 * steps[j])
+
+    def test_jacobian_shape_is_n_residuals_by_three(self):
+        """The analytic Jacobian has shape (N_residuals, 3) — one row per measurement bin, one column per fit parameter (log n_α, log T_α, Δv)."""
+        self.assertEqual(self.analytic_jacobian.shape, (self.alpha_ctx.count_rate.size, 3))
+
+    def test_log_density_column_equals_alpha_only_rate_times_deadtime_squared(self):
+        """Because the rate is linear in n_α, the analytic ∂(observable)/∂(log n_α) column equals 𝒟²(R_total) · R_α exactly with no quadrature slack — an identity check independent of finite-difference noise."""
+        from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+            model_solar_wind_ideal_coincidence_rates,
+        )
+        alpha_only_params = SolarWindParams(
+            density=_TRUE_ALPHA_DENSITY_CM3,
+            bulk_velocity_rtn=_TRUE_PROTON_VELOCITY_RTN + _TRUE_DELTA_V_KM_S * _B_HAT_RTN,
+            temperature=_TRUE_ALPHA_TEMPERATURE_K,
+            mass=ALPHA_PARTICLE_MASS_KG,
+        )
+        alpha_rate, _ = model_solar_wind_ideal_coincidence_rates(
+            alpha_only_params, self.alpha_ctx
+        )
+        total_rate = self.proton_true_rate + alpha_rate
+        deadtime_squared = deadtime_factor(total_rate) ** 2
+        np.testing.assert_allclose(
+            self.analytic_jacobian[:, 0],
+            deadtime_squared * alpha_rate,
+            rtol=1.0e-12,
+            atol=0.0,
+        )
+
+    def test_log_temperature_column_matches_finite_difference(self):
+        """The analytic ∂residuals/∂(log T_α) column matches central differences within 5% — the threshold matches the proton forward-model Jacobian tolerance."""
+        np.testing.assert_allclose(
+            self.analytic_jacobian[:, 1],
+            self.numerical_jacobian[:, 1],
+            rtol=5.0e-2,
+            atol=1.0e-3,
+        )
+
+    def test_delta_v_column_matches_finite_difference(self):
+        """The analytic ∂residuals/∂(Δv) column matches central differences within 5% — derived from the 5-D forward-model Jacobian projected onto B̂, inherits the same boundary-term residual."""
+        np.testing.assert_allclose(
+            self.analytic_jacobian[:, 2],
+            self.numerical_jacobian[:, 2],
+            rtol=5.0e-2,
+            atol=1.0e-3,
+        )
+
+
+class TestAlphaEvaluatorCachesEvaluation(
+    _SyntheticAlphaSpectrumFixture, unittest.TestCase
+):
+    """Tests for `_AlphaEvaluator._refresh` — caches the last (residuals, jacobian) so scipy.least_squares' separate `fun` and `jac` callbacks share one forward-model evaluation per state."""
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.alpha_ctx = build_solar_wind_fit_context(
+            count_rate=cls.observed_count_rate,
+            esa_voltage=cls.voltage,
+            swapi_response=cls.response,
+            central_effective_area_scale=1.0,
+            rotation_matrices=cls.rotation_matrices,
+            mass_kg=ALPHA_PARTICLE_MASS_KG,
+            mass_per_charge_m_p_per_e=ALPHA_MASS_PER_CHARGE_M_P_PER_E,
+        )
+        cls.x0 = np.array(
+            [
+                np.log(_TRUE_ALPHA_DENSITY_CM3),
+                np.log(_TRUE_ALPHA_TEMPERATURE_K),
+                _TRUE_DELTA_V_KM_S,
+            ]
+        )
+
+    def test_residuals_then_jacobian_at_same_state_share_one_forward_model_call(self):
+        """Calling residuals(x) and then jacobian(x) at the same x triggers exactly one underlying forward-model call — the second call hits the cache."""
+        evaluator = _AlphaEvaluator(
+            proton_bulk=_TRUE_PROTON_VELOCITY_RTN,
+            magnetic_field_direction=_B_HAT_RTN,
+            proton_true_rate=self.proton_true_rate,
+            alpha_ctx=self.alpha_ctx,
+        )
+        with patch.object(
+            alpha_module,
+            "model_solar_wind_ideal_coincidence_rates",
+            wraps=alpha_module.model_solar_wind_ideal_coincidence_rates,
+        ) as wrapped:
+            evaluator.residuals(self.x0)
+            evaluator.jacobian(self.x0)
+        self.assertEqual(wrapped.call_count, 1)
+
+    def test_residuals_at_different_states_each_trigger_forward_model_call(self):
+        """Calling residuals at two distinct states triggers two forward-model calls — the cache is keyed on the exact state vector."""
+        evaluator = _AlphaEvaluator(
+            proton_bulk=_TRUE_PROTON_VELOCITY_RTN,
+            magnetic_field_direction=_B_HAT_RTN,
+            proton_true_rate=self.proton_true_rate,
+            alpha_ctx=self.alpha_ctx,
+        )
+        x1 = self.x0.copy()
+        x2 = self.x0.copy()
+        x2[2] += 1.0
+        with patch.object(
+            alpha_module,
+            "model_solar_wind_ideal_coincidence_rates",
+            wraps=alpha_module.model_solar_wind_ideal_coincidence_rates,
+        ) as wrapped:
+            evaluator.residuals(x1)
+            evaluator.residuals(x2)
+        self.assertEqual(wrapped.call_count, 2)
+
+
+# ----- LM call wiring: analytic Jacobian passed to scipy -------------------
+
+
+class TestFitAlphaMomentsPassesAnalyticJacobianToLM(
+    _SyntheticAlphaSpectrumFixture, unittest.TestCase
+):
+    """Tests for `fit_solar_wind_alpha_moments` — verifies scipy.optimize.least_squares is invoked with `jac=...` (rather than the previous finite-difference `diff_step=...` path)."""
+
+    def test_least_squares_receives_jac_callable_and_no_diff_step(self):
+        """LM is called with a `jac` callable (the evaluator's analytic Jacobian) and without `diff_step` — finite-difference Jacobian estimation is no longer needed."""
+        proton_moments = _build_proton_fit_result()
+
+        mock_result = MagicMock()
+        mock_result.x = np.array(
+            [np.log(_TRUE_ALPHA_DENSITY_CM3), np.log(_TRUE_ALPHA_TEMPERATURE_K), 0.0]
+        )
+        mock_result.fun = np.zeros(_N_MEAS)
+        mock_result.jac = np.zeros((_N_MEAS, 3))
+        mock_result.success = True
+
+        with patch.object(
+            alpha_module,
+            "_alpha_initial_guess",
+            return_value=(
+                _TRUE_ALPHA_DENSITY_CM3,
+                _TRUE_ALPHA_TEMPERATURE_K,
+                0.0,
+                np.array([10, 11, 12]),
+            ),
+        ), patch.object(
+            alpha_module._AlphaEvaluator,
+            "residuals",
+            return_value=np.zeros(_N_MEAS),
+        ), patch.object(
+            alpha_module.scipy.optimize,
+            "least_squares",
+            return_value=mock_result,
+        ) as mock_lm:
+            fit_solar_wind_alpha_moments(
+                count_rate=self.observed_count_rate,
+                esa_voltage=self.voltage,
+                measurement_time=np.zeros(_N_MEAS),
+                swapi_response=self.response,
+                proton_moments=proton_moments,
+                magnetic_field_direction=_B_HAT_RTN,
+                alpha_effective_area_scale=1.0,
+                proton_effective_area_scale=1.0,
+                rotation_matrices=self.rotation_matrices,
+            )
+
+        kwargs = mock_lm.call_args.kwargs
+        self.assertIn("jac", kwargs)
+        self.assertTrue(callable(kwargs["jac"]))
+        self.assertNotIn("diff_step", kwargs)
+        self.assertEqual(kwargs["method"], "lm")
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/science/solar_wind/alpha/test_utils.py b/tests/swapi/l3a/science/solar_wind/alpha/test_utils.py
new file mode 100644
index 000000000..4447206b9
--- /dev/null
+++ b/tests/swapi/l3a/science/solar_wind/alpha/test_utils.py
@@ -0,0 +1,112 @@
+import unittest
+
+import numpy as np
+
+from imap_l3_processing.constants import (
+    ALPHA_PARTICLE_CHARGE_COULOMBS,
+    ALPHA_PARTICLE_MASS_KG,
+    METERS_PER_KILOMETER,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.alpha.utils import (
+    esa_voltage_to_alpha_speed,
+    get_alpha_peak_indices,
+)
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+
+
+def _analytic_speed_km_per_s(
+    voltage: float, mass_kg: float, charge_c: float
+) -> float:
+    return float(
+        np.sqrt(2 * SWAPI_K_FACTOR * charge_c * abs(voltage) / mass_kg)
+        / METERS_PER_KILOMETER
+    )
+
+
+class TestEsaVoltageToAlphaSpeed(unittest.TestCase):
+    """Tests for `esa_voltage_to_alpha_speed`."""
+
+    def test_matches_analytical_formula(self):
+        """A sweep of representative ESA voltages reproduces the closed-form `sqrt(2 K q V / m)` alpha speed to machine precision."""
+        for V in [200.0, 1000.0, 4000.0]:
+            with self.subTest(voltage=V):
+                np.testing.assert_allclose(
+                    esa_voltage_to_alpha_speed(V),
+                    _analytic_speed_km_per_s(
+                        V, ALPHA_PARTICLE_MASS_KG, ALPHA_PARTICLE_CHARGE_COULOMBS
+                    ),
+                    rtol=1e-12,
+                )
+
+    def test_handles_negative_voltage(self):
+        """A negative ESA voltage yields the same alpha speed as its positive counterpart, since the conversion depends on magnitude."""
+        np.testing.assert_allclose(
+            esa_voltage_to_alpha_speed(-1000.0), esa_voltage_to_alpha_speed(1000.0)
+        )
+
+
+class TestGetAlphaPeakIndices(unittest.TestCase):
+    """Tests for `get_alpha_peak_indices`; SWAPI energies decrease with index so the alpha bump sits at *lower* indices than the proton peak, and the returned slice indexes into that ascending-index axis."""
+
+    PROTON_PEAK_INDEX = 50
+    ALPHA_PEAK_INDEX = 30
+
+    def _decreasing_energy_grid_with_alpha_bump(self) -> tuple[np.ndarray, np.ndarray]:
+        """SWAPI-style decreasing energy grid plus a Gaussian alpha bump
+        centered at `ALPHA_PEAK_INDEX`."""
+        energies = np.linspace(20_000.0, 50.0, 72)
+        n_alpha = 200.0 * np.exp(
+            -((np.arange(72) - self.ALPHA_PEAK_INDEX) ** 2) / 9.0
+        )
+        return energies, n_alpha
+
+    def test_returns_slice_containing_alpha_peak(self):
+        """Given a clean Gaussian alpha bump above the proton peak, the returned slice brackets the bump's center index."""
+        energies, residuals = self._decreasing_energy_grid_with_alpha_bump()
+        peak_slice = get_alpha_peak_indices(
+            residuals, energies, proton_peak_index=self.PROTON_PEAK_INDEX
+        )
+        self.assertLessEqual(peak_slice.start, self.ALPHA_PEAK_INDEX)
+        self.assertGreater(peak_slice.stop, self.ALPHA_PEAK_INDEX)
+
+    def test_slice_low_index_edge_sits_at_4x_proton_energy(self):
+        """The slice's low-index (high-energy) edge is anchored to the first bin at or below 4x the proton-peak energy, with the bin one index lower exceeding that cap."""
+        energies, residuals = self._decreasing_energy_grid_with_alpha_bump()
+        peak_slice = get_alpha_peak_indices(
+            residuals, energies, proton_peak_index=self.PROTON_PEAK_INDEX
+        )
+        max_alpha_energy = 4.0 * energies[self.PROTON_PEAK_INDEX]
+
+        self.assertLessEqual(energies[peak_slice.start], max_alpha_energy)
+        if peak_slice.start > 0:
+            self.assertGreater(energies[peak_slice.start - 1], max_alpha_energy)
+
+    def test_raises_when_proton_peak_at_high_energy_edge(self):
+        """When the proton peak is at index 0 there are no higher-energy bins for the alpha bump to occupy, and the function raises rather than returning a degenerate slice."""
+        energies, residuals = self._decreasing_energy_grid_with_alpha_bump()
+        with self.assertRaises(Exception) as ctx:
+            get_alpha_peak_indices(residuals, energies, proton_peak_index=0)
+        self.assertIn("Alpha peak not found", str(ctx.exception))
+
+    def test_raises_when_residuals_are_monotonic_in_high_energy_region(self):
+        """Residuals that increase monotonically with energy have no local maximum above the proton peak, so the function raises instead of locating a fake bump."""
+        energies = np.linspace(20_000.0, 50.0, 72)
+        monotonic_residuals = np.linspace(1.0, 100.0, 72)
+        with self.assertRaises(Exception) as ctx:
+            get_alpha_peak_indices(
+                monotonic_residuals, energies, proton_peak_index=self.PROTON_PEAK_INDEX
+            )
+        self.assertIn("Alpha peak not found", str(ctx.exception))
+
+    def test_rejects_ascending_energy_grid(self):
+        """An L1B-style ascending energy axis violates the SWAPI calling convention and is rejected at the top of the function with an AssertionError."""
+        ascending_energies = np.linspace(50.0, 20_000.0, 72)
+        residuals = np.zeros(72)
+        with self.assertRaises(AssertionError):
+            get_alpha_peak_indices(
+                residuals, ascending_energies, proton_peak_index=self.PROTON_PEAK_INDEX
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/science/solar_wind/proton/__init__.py b/tests/swapi/l3a/science/solar_wind/proton/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/swapi/l3a/science/solar_wind/proton/test_basin_hopping.py b/tests/swapi/l3a/science/solar_wind/proton/test_basin_hopping.py
new file mode 100644
index 000000000..c89fdba76
--- /dev/null
+++ b/tests/swapi/l3a/science/solar_wind/proton/test_basin_hopping.py
@@ -0,0 +1,212 @@
+import unittest
+from unittest.mock import Mock, patch
+
+import numpy as np
+
+from imap_l3_processing.constants import PROTON_MASS_KG
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    SolarWindFitContext,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.optimizer import (
+    OptimizeSolarWindParamsResult,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton import basin_hopping
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.basin_hopping import (
+    _MAX_BASIN_REFINE_ITERS,
+    _ROTATED_RMSE_RATIO_THRESHOLD,
+    escape_local_minimum,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
+from tests.swapi._helpers import proton_params as _shared_proton_params
+
+
+# Spin-axis-mirror tests assume the slow-wind bulk lies along -R̂ (the spin
+# axis), so the file-local default puts the velocity along the RTN R-axis.
+def _proton_params(
+    density: float = 5.0,
+    velocity_rtn=(-450.0, 0.0, 0.0),
+    temperature: float = 1.0e5,
+) -> SolarWindParams:
+    return _shared_proton_params(
+        density=density,
+        velocity_rtn=velocity_rtn,
+        temperature=temperature,
+    )
+
+
+def _single_sweep_ctx(
+    spin_axis_column=np.array([0.0, 1.0, 0.0]),
+) -> SolarWindFitContext:
+    """Single-sweep context whose +Y_inst column equals `spin_axis_column`,
+    so `average_spin_axis_rtn(ctx.rotation_matrices)` returns that axis."""
+    rotation = np.eye(3)
+    rotation[:, 1] = spin_axis_column
+    return SolarWindFitContext(
+        count_rate=np.zeros(1),
+        esa_voltage=np.array([1000.0]),
+        response_grids=[None],
+        rotation_matrices=rotation[np.newaxis, :, :],
+        mass_kg=PROTON_MASS_KG,
+    )
+
+
+def _result(sw: SolarWindParams, mse: float) -> OptimizeSolarWindParamsResult:
+    """Build an `OptimizeSolarWindParamsResult` mock whose `.mse` returns `mse`
+    and whose `.sw_params` returns `sw`. Uses `Mock(spec=...)` so the test does
+    not depend on how `.mse` is computed internally."""
+    mock = Mock(spec=OptimizeSolarWindParamsResult)
+    mock.sw_params = sw
+    mock.mse = mse
+    return mock
+
+
+class TestEscapeLocalMinimum(unittest.TestCase):
+    """Tests for `escape_local_minimum`; mocks `_flipped_seed` and `optimize_solar_wind_params` so cheap-gate, acceptance, and iteration-cap logic are exercised in isolation from the forward model."""
+
+    # The cheap-gate condition is `flipped_mse >= threshold² × current.mse`;
+    # squaring threshold compares MSE against an RMSE ratio.
+    _GATE_FACTOR = _ROTATED_RMSE_RATIO_THRESHOLD**2
+
+    # ---- Cheap-gate early exit: no LM-2 call when flipped seed is too bad ----
+
+    def test_returns_lm1_unchanged_when_flipped_seed_far_worse(self):
+        """A flipped seed whose MSE is well past the threshold²×current gate bails out immediately, leaving LM-1 as the returned result without ever invoking the LM-2 optimizer."""
+        ctx = _single_sweep_ctx()
+        lm1 = _result(_proton_params(), mse=1.0)
+        flipped_mse_far_above_gate = lm1.mse * self._GATE_FACTOR * 10.0
+        flipped_params = _proton_params(velocity_rtn=(450.0, 0.0, 0.0))
+
+        with patch.object(
+            basin_hopping,
+            "_flipped_seed",
+            return_value=(flipped_mse_far_above_gate, flipped_params),
+        ) as mock_flipped, patch.object(
+            basin_hopping, "optimize_solar_wind_params"
+        ) as mock_opt:
+            out = escape_local_minimum(lm1, ctx)
+
+        self.assertIs(out, lm1)
+        # `_flipped_seed` ran exactly once (loop bailed after the gate check).
+        self.assertEqual(mock_flipped.call_count, 1)
+        # The optimizer was never re-run on the flipped seed.
+        mock_opt.assert_not_called()
+
+    def test_returns_lm1_unchanged_when_flipped_seed_exactly_at_gate(self):
+        """A flipped seed whose MSE exactly equals the gate threshold also short-circuits, because the gate comparison is `>=` rather than strictly greater than."""
+        ctx = _single_sweep_ctx()
+        lm1 = _result(_proton_params(), mse=1.0)
+        flipped_mse_at_gate = lm1.mse * self._GATE_FACTOR
+
+        with patch.object(
+            basin_hopping,
+            "_flipped_seed",
+            return_value=(flipped_mse_at_gate, _proton_params()),
+        ), patch.object(
+            basin_hopping, "optimize_solar_wind_params"
+        ) as mock_opt:
+            out = escape_local_minimum(lm1, ctx)
+
+        self.assertIs(out, lm1)
+        mock_opt.assert_not_called()
+
+    # ---- LM-2 accepted when it lands at strictly lower MSE ----
+
+    def test_returns_lm2_when_lm2_mse_is_lower(self):
+        """When the flipped seed clears the cheap gate and LM-2 converges to a strictly lower MSE than LM-1, the function adopts the LM-2 result and continues iterating until the next gate check bails out."""
+        ctx = _single_sweep_ctx()
+        lm1 = _result(_proton_params(), mse=10.0)
+        better_params = _proton_params(velocity_rtn=(450.0, 0.0, 0.0))
+        better_lm2 = _result(better_params, mse=1.0)
+
+        # Iteration 1: flipped seed clears the gate, LM-2 lands lower → accepted.
+        # Iteration 2: simulate "next flipped seed is far worse than the new
+        # current" so the loop terminates cleanly via the cheap gate.
+        flipped_seed_mse_iter1 = lm1.mse * (self._GATE_FACTOR / 2.0)
+        flipped_seed_mse_iter2 = better_lm2.mse * (self._GATE_FACTOR * 2.0)
+
+        with patch.object(
+            basin_hopping,
+            "_flipped_seed",
+            side_effect=[
+                (flipped_seed_mse_iter1, better_params),
+                (flipped_seed_mse_iter2, better_params),
+            ],
+        ) as mock_flipped, patch.object(
+            basin_hopping,
+            "optimize_solar_wind_params",
+            return_value=better_lm2,
+        ) as mock_opt:
+            out = escape_local_minimum(lm1, ctx)
+
+        self.assertIs(out, better_lm2)
+        # LM-2 ran exactly once (iteration 2 exited via the cheap gate).
+        self.assertEqual(mock_opt.call_count, 1)
+        # Two iterations of the loop ran; iter 2 short-circuited at the gate.
+        self.assertEqual(mock_flipped.call_count, 2)
+
+    # ---- LM-2 rejected when it converges to higher MSE ----
+
+    def test_returns_lm1_when_lm2_mse_is_higher(self):
+        """When LM-2 clears the cheap gate but converges to a higher MSE than LM-1, the function rejects the flipped basin and returns the original LM-1 result."""
+        ctx = _single_sweep_ctx()
+        lm1 = _result(_proton_params(), mse=1.0)
+        worse_params = _proton_params(velocity_rtn=(450.0, 0.0, 0.0))
+        worse_lm2 = _result(worse_params, mse=5.0)
+
+        # Flipped seed clears the cheap gate (within threshold²×current).
+        flipped_seed_mse = lm1.mse * (self._GATE_FACTOR / 2.0)
+        with patch.object(
+            basin_hopping,
+            "_flipped_seed",
+            return_value=(flipped_seed_mse, worse_params),
+        ), patch.object(
+            basin_hopping,
+            "optimize_solar_wind_params",
+            return_value=worse_lm2,
+        ) as mock_opt:
+            out = escape_local_minimum(lm1, ctx)
+
+        self.assertIs(out, lm1)
+        self.assertEqual(mock_opt.call_count, 1)
+
+    # ---- Iteration cap: at most _MAX_BASIN_REFINE_ITERS LM-2 calls ----
+
+    def test_runs_at_most_max_basin_refine_iters_lm2_calls(self):
+        """Feeding a strictly-decreasing MSE chain that would otherwise accept indefinitely confirms the loop is hard-bounded at `_MAX_BASIN_REFINE_ITERS` LM-2 calls and returns the result accepted in the final allowed iteration."""
+        ctx = _single_sweep_ctx()
+        # Strictly-decreasing MSE chain so every iteration accepts the LM-2
+        # result. If the loop weren't bounded, this would never terminate.
+        # Use `1.0 / (i+1)` so MSE stays strictly positive for any chain length.
+        chain_length = _MAX_BASIN_REFINE_ITERS + 4
+        results = [
+            _result(
+                _proton_params(velocity_rtn=(-450.0 + i, 0.0, 0.0)),
+                mse=1.0 / (i + 1),
+            )
+            for i in range(chain_length)
+        ]
+        lm1 = results[0]
+        gate_factor = self._GATE_FACTOR
+        with patch.object(
+            basin_hopping,
+            "_flipped_seed",
+            # Always within gate (factor / 2 < gate_factor), always with a fresh seed.
+            side_effect=[
+                (results[i].mse * (gate_factor / 2.0), results[i].sw_params)
+                for i in range(chain_length - 1)
+            ],
+        ), patch.object(
+            basin_hopping,
+            "optimize_solar_wind_params",
+            side_effect=results[1:],
+        ) as mock_opt:
+            out = escape_local_minimum(lm1, ctx)
+
+        # Pin the cap from the source constant — never hardcode the number.
+        self.assertEqual(mock_opt.call_count, _MAX_BASIN_REFINE_ITERS)
+        # Returns the LM-2 result accepted in the final allowed iteration.
+        self.assertIs(out, results[_MAX_BASIN_REFINE_ITERS])
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/science/solar_wind/proton/test_fit_model.py b/tests/swapi/l3a/science/solar_wind/proton/test_fit_model.py
new file mode 100644
index 000000000..af4a35324
--- /dev/null
+++ b/tests/swapi/l3a/science/solar_wind/proton/test_fit_model.py
@@ -0,0 +1,368 @@
+import unittest
+from unittest.mock import patch
+
+import numpy as np
+
+from imap_l3_processing.constants import (
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    build_solar_wind_fit_context,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.optimizer import (
+    OptimizeSolarWindParamsResult,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_model import (
+    fit_solar_wind_proton_model,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    N_STATE,
+    SolarWindParams,
+)
+from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
+from tests.swapi._helpers import load_swapi_response, synthesize_count_rates
+
+# Mean SWAPI L2 coarse-sweep voltages (V), descending — a 62-bin sweep that
+# covers the proton speed range densely. Identical to the set used by
+# `docs/swapi/figure_src/plot_fit_accuracy.py`.
+_VOLTAGES_PER_SWEEP = np.array(
+    [
+        9895.52, 9088.69, 8348.80, 7667.55, 7042.16, 6469.31, 5941.77, 5457.31,
+        5013.22, 4603.65, 4230.77, 3886.92, 3569.16, 3278.72, 3011.13, 2766.25,
+        2539.54, 2333.83, 2144.24, 1969.31, 1808.74, 1660.86, 1525.75, 1401.82,
+        1287.58, 1182.24, 1085.15, 995.55, 914.31, 839.94, 771.70, 709.46,
+        651.59, 598.47, 549.91, 505.12, 463.89, 425.92, 391.18, 359.35, 329.94,
+        303.02, 278.25, 255.55, 234.77, 215.61, 197.95, 181.82, 167.04, 153.46,
+        140.91, 129.50, 118.91, 109.20, 100.30, 92.11, 84.61, 77.73, 71.40,
+        65.59, 60.23, 55.34,
+    ]
+)
+_N_BINS_PER_SWEEP = len(_VOLTAGES_PER_SWEEP)
+_N_SWEEPS = 5
+_SWEEP_DURATION_S = 12.0
+_SAMPLE_TIME_PER_BIN_S = _SWEEP_DURATION_S / 72
+# Typical IMAP spin period; matches `docs/swapi/figure_src/plot_fit_accuracy.py`.
+_SPIN_PERIOD_S = 15.13
+
+# Anchor SWAPI→RTN rotation matrix near 2026-01-01, lifted from
+# `docs/swapi/figure_src/plot_fit_accuracy.py`. The literal block is in
+# RTN→SWAPI orientation; transposing converts to SWAPI→RTN. Per-bin matrices
+# are produced by spinning this anchor about its own +Y column (the spin axis
+# in RTN) at the SWAPI spin period.
+_ANCHOR_ROTATION_MATRIX = np.array(
+    [
+        [+0.0705, +0.9157, +0.3955],
+        [-0.9968, +0.0792, -0.0057],
+        [-0.0365, -0.3939, +0.9184],
+    ]
+).T
+_ANCHOR_TIME_S = 0.5 * _SWEEP_DURATION_S
+# Negative sign chosen so R(t) = anchor @ Rot(δφ, spin_axis_RTN) reproduces
+# independent SPICE-derived sweep midpoints over a 5-sweep cycle (see
+# `docs/swapi/figure_src/plot_fit_accuracy.py`).
+_SPIN_OMEGA_RAD_S = -2.0 * np.pi / _SPIN_PERIOD_S
+
+# The +Y column of `_ANCHOR_ROTATION_MATRIX` is the SWAPI spin axis expressed
+# in RTN. For a proper rotation matrix it must be unit-norm; assert that here
+# so the bulk-velocity construction below can use it directly.
+assert np.isclose(
+    np.linalg.norm(_ANCHOR_ROTATION_MATRIX[:, 1]), 1.0, atol=1e-3
+), "expected +Y column of anchor rotation to be unit-norm"
+
+# Ground-truth solar-wind parameters used for the parameter-recovery test.
+# Moderate-speed slow-stream proton population — well inside the SWAPI energy
+# range, well within the SG passband elevation, and warm enough that the LM
+# fit is not numerically marginal.
+#
+# The bulk velocity is anti-parallel to the synthetic spin axis (the +Y column
+# of `_ANCHOR_ROTATION_MATRIX`, which lies near -R̂_RTN). This puts the wind
+# *into* the SWAPI aperture; using arbitrary RTN-frame velocity components
+# would point the wind at the back of the instrument and zero the synthetic
+# count rates.
+_TRUE_DENSITY_CM3 = 5.0
+_TRUE_TEMPERATURE_K = 1.0e5
+_TRUE_BULK_SPEED_KM_S = 450.0
+_TRUE_BULK_VELOCITY_RTN_KM_S = (
+    -_TRUE_BULK_SPEED_KM_S * _ANCHOR_ROTATION_MATRIX[:, 1]
+)
+
+
+def _per_bin_rotation_matrices() -> np.ndarray:
+    """Synthesize plausible per-bin SWAPI→RTN matrices for `_N_SWEEPS` sweeps;
+    details don't affect what's being tested."""
+    sweep_index = np.repeat(
+        np.arange(_N_SWEEPS), _N_BINS_PER_SWEEP
+    )
+    bin_index_in_sweep = np.tile(
+        np.arange(1, _N_BINS_PER_SWEEP + 1), _N_SWEEPS
+    )
+    sample_times_s = (
+        sweep_index * _SWEEP_DURATION_S
+        + bin_index_in_sweep * _SAMPLE_TIME_PER_BIN_S
+    )
+
+    spin_axis = _ANCHOR_ROTATION_MATRIX[:, 1] / np.linalg.norm(
+        _ANCHOR_ROTATION_MATRIX[:, 1]
+    )
+    delta_phi = _SPIN_OMEGA_RAD_S * (sample_times_s - _ANCHOR_TIME_S)
+
+    ax, ay, az = spin_axis
+    K = np.array([[0.0, -az, ay], [az, 0.0, -ax], [-ay, ax, 0.0]])
+    sin_dp = np.sin(delta_phi)[:, None, None]
+    one_minus_cos = (1.0 - np.cos(delta_phi))[:, None, None]
+    rot = np.eye(3) + sin_dp * K + one_minus_cos * (K @ K)
+    return rot @ _ANCHOR_ROTATION_MATRIX
+
+
+def _build_context(count_rate, esa_voltage, swapi_response, rotation_matrices):
+    return build_solar_wind_fit_context(
+        count_rate=count_rate,
+        esa_voltage=esa_voltage,
+        swapi_response=swapi_response,
+        central_effective_area_scale=1.0,
+        rotation_matrices=rotation_matrices,
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    )
+
+
+def _build_synthetic_fit_context(truth_params: SolarWindParams):
+    """Build a SwapiResponse, per-bin rotation matrices, and a populated
+    `SolarWindFitContext` whose count rates are forward-modelled from
+    `truth_params`. Returns `(swapi_response, rotation_matrices, fit_ctx)`."""
+    all_voltages = np.tile(_VOLTAGES_PER_SWEEP, _N_SWEEPS)
+    swapi_response = load_swapi_response(warm_cache_voltages=all_voltages)
+    rotation_matrices = _per_bin_rotation_matrices()
+
+    # Build a context with placeholder rates first, then forward-model the
+    # synthetic rates at the truth, then rebuild the context with those rates.
+    # Two-step pattern keeps the rotation-matrix / response-grid wiring
+    # identical between forward-model and fit.
+    placeholder_ctx = _build_context(
+        count_rate=np.ones_like(all_voltages),
+        esa_voltage=all_voltages,
+        swapi_response=swapi_response,
+        rotation_matrices=rotation_matrices,
+    )
+    synthesized_rates = synthesize_count_rates(placeholder_ctx, truth_params)
+    fit_ctx = _build_context(
+        count_rate=synthesized_rates,
+        esa_voltage=all_voltages,
+        swapi_response=swapi_response,
+        rotation_matrices=rotation_matrices,
+    )
+    return swapi_response, rotation_matrices, fit_ctx
+
+
+def _truth_params() -> SolarWindParams:
+    return SolarWindParams(
+        density=_TRUE_DENSITY_CM3,
+        bulk_velocity_rtn=_TRUE_BULK_VELOCITY_RTN_KM_S.copy(),
+        temperature=_TRUE_TEMPERATURE_K,
+        mass=PROTON_MASS_KG,
+    )
+
+
+class _ProtonFitFixture(unittest.TestCase):
+    """Loads the SwapiResponse and a populated `SolarWindFitContext` with
+    forward-modelled count rates for the moderate-speed truth case, then runs
+    the fitter once and stores `cls.result`."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.truth_params = _truth_params()
+        (
+            cls.swapi_response,
+            cls.rotation_matrices,
+            cls.fit_ctx,
+        ) = _build_synthetic_fit_context(truth_params=cls.truth_params)
+        cls.result = fit_solar_wind_proton_model(cls.fit_ctx)
+
+
+class TestFitSolarWindProtonModelEndToEnd(_ProtonFitFixture):
+    """Tests for `fit_solar_wind_proton_model`; noise-free parameter-recovery sanity check against truth."""
+
+    def test_recovers_density(self):
+        """Fitting noise-free synthesized rates recovers the truth density within 1%."""
+        self.assertAlmostEqual(
+            self.result.density.nominal_value,
+            _TRUE_DENSITY_CM3,
+            delta=0.01 * _TRUE_DENSITY_CM3,
+        )
+
+    def test_recovers_temperature(self):
+        """Fitting noise-free synthesized rates recovers the truth temperature within 1%."""
+        self.assertAlmostEqual(
+            self.result.temperature.nominal_value,
+            _TRUE_TEMPERATURE_K,
+            delta=0.01 * _TRUE_TEMPERATURE_K,
+        )
+
+    def test_recovers_bulk_velocity_components(self):
+        """Fitting noise-free synthesized rates recovers all three RTN bulk-velocity components within 1 km/s."""
+        nominal = self.result.bulk_velocity_rtn_nominal()
+        np.testing.assert_allclose(
+            nominal, _TRUE_BULK_VELOCITY_RTN_KM_S, atol=1.0
+        )
+
+    def test_bad_fit_flag_is_none_on_successful_convergence(self):
+        """A clean LM convergence on noise-free data leaves the bad-fit flag cleared (`SwapiL3Flags.NONE`)."""
+        self.assertEqual(self.result.bad_fit_flag, SwapiL3Flags.NONE)
+
+
+class TestProtonSolarWindFitResultPublicAPI(_ProtonFitFixture):
+    """Tests for `ProtonSolarWindFitResult.bulk_velocity_rtn_nominal` and `bulk_velocity_rtn_covariance` accessors."""
+
+    def test_bulk_velocity_rtn_nominal_is_three_component_vector(self):
+        """The nominal-velocity accessor returns a length-3 RTN vector."""
+        nominal = self.result.bulk_velocity_rtn_nominal()
+        self.assertEqual(nominal.shape, (3,))
+
+    def test_bulk_velocity_rtn_nominal_matches_per_component_nominal_values(
+        self,
+    ):
+        """The nominal-velocity vector matches the per-component `.nominal_value` of the stored UFloat triple."""
+        per_component = np.array(
+            [v.nominal_value for v in self.result.bulk_velocity_rtn]
+        )
+        np.testing.assert_array_equal(
+            self.result.bulk_velocity_rtn_nominal(), per_component
+        )
+
+    def test_bulk_velocity_rtn_covariance_is_three_by_three(self):
+        """The covariance accessor returns a 3x3 matrix matching the RTN component count."""
+        covariance = self.result.bulk_velocity_rtn_covariance()
+        self.assertEqual(covariance.shape, (3, 3))
+
+    def test_bulk_velocity_rtn_covariance_is_symmetric(self):
+        """The returned velocity covariance matrix is symmetric to numerical tolerance."""
+        covariance = self.result.bulk_velocity_rtn_covariance()
+        np.testing.assert_allclose(covariance, covariance.T, atol=1e-12)
+
+    def test_bulk_velocity_rtn_covariance_is_positive_semidefinite(self):
+        """The returned velocity covariance matrix is positive semidefinite (all eigenvalues non-negative)."""
+        covariance = self.result.bulk_velocity_rtn_covariance()
+        eigenvalues = np.linalg.eigvalsh(covariance)
+        self.assertGreaterEqual(eigenvalues.min(), -1e-9)
+
+    def test_covariance_diagonal_matches_per_component_variance(self):
+        """Each diagonal entry of the velocity covariance equals the square of the corresponding UFloat `std_dev`."""
+        covariance = self.result.bulk_velocity_rtn_covariance()
+        for i, ufloat_value in enumerate(self.result.bulk_velocity_rtn):
+            self.assertAlmostEqual(
+                covariance[i, i], ufloat_value.std_dev ** 2, places=10
+            )
+
+
+class TestQualityFlagBranches(unittest.TestCase):
+    """Tests for `fit_solar_wind_proton_model`; quality-flag branches: optimizer failure → `FIT_ERROR` (NaN moments), high fitted temperature → `BAD_FIT` (moments kept)."""
+
+    @classmethod
+    def setUpClass(cls):
+        _, _, cls.fit_ctx = _build_synthetic_fit_context(
+            truth_params=_truth_params()
+        )
+
+    def _patch_optimizer_with_result(self, optimize_result):
+        return patch(
+            "imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_model.optimize_solar_wind_params",
+            return_value=optimize_result,
+        )
+
+    def test_fit_error_flag_when_optimizer_reports_failure(self):
+        """When the underlying optimizer returns `success=False`, the result's bad-fit flag is `SwapiL3Flags.FIT_ERROR` and the moments are NaN-filled."""
+        failed_result = OptimizeSolarWindParamsResult(
+            sw_params=SolarWindParams(
+                density=1.0,
+                bulk_velocity_rtn=np.array([-1.0, 0.0, 0.0]),
+                temperature=1.0,
+                mass=PROTON_MASS_KG,
+            ),
+            residuals=np.zeros(_N_BINS_PER_SWEEP * _N_SWEEPS),
+            jacobian=np.zeros(
+                (_N_BINS_PER_SWEEP * _N_SWEEPS, N_STATE)
+            ),
+            success=False,
+        )
+        with self._patch_optimizer_with_result(failed_result):
+            result = fit_solar_wind_proton_model(self.fit_ctx)
+        self.assertEqual(result.bad_fit_flag, SwapiL3Flags.FIT_ERROR)
+        self.assertTrue(np.isnan(result.density.nominal_value))
+        self.assertTrue(np.isnan(result.temperature.nominal_value))
+
+    def test_bad_fit_flag_when_temperature_above_threshold(self):
+        """A converged fit whose temperature exceeds 5e5 K is flagged `BAD_FIT` and its moments are NaN-filled, distinguishing it from a clean fit but matching `FIT_ERROR`'s fill-value contract."""
+        too_hot_temperature = 6.0e5
+        too_hot_result = OptimizeSolarWindParamsResult(
+            sw_params=SolarWindParams(
+                density=_TRUE_DENSITY_CM3,
+                bulk_velocity_rtn=_TRUE_BULK_VELOCITY_RTN_KM_S.copy(),
+                temperature=too_hot_temperature,
+                mass=PROTON_MASS_KG,
+            ),
+            residuals=np.zeros(_N_BINS_PER_SWEEP * _N_SWEEPS),
+            jacobian=np.zeros(
+                (_N_BINS_PER_SWEEP * _N_SWEEPS, N_STATE)
+            ),
+            success=True,
+        )
+        with patch(
+            "imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_model.escape_local_minimum",
+            return_value=too_hot_result,
+        ):
+            result = fit_solar_wind_proton_model(self.fit_ctx)
+        self.assertEqual(result.bad_fit_flag, int(SwapiL3Flags.BAD_FIT))
+        self.assertTrue(np.isnan(result.density.nominal_value))
+        self.assertTrue(np.isnan(result.temperature.nominal_value))
+        for component in result.bulk_velocity_rtn:
+            self.assertTrue(np.isnan(component.nominal_value))
+
+
+class TestPipelineOrder(unittest.TestCase):
+    """Tests for `fit_solar_wind_proton_model`; verifies basin-hopping output supersedes the LM-1 result."""
+
+    @classmethod
+    def setUpClass(cls):
+        _, _, cls.fit_ctx = _build_synthetic_fit_context(
+            truth_params=_truth_params()
+        )
+
+    def test_construct_fit_result_uses_post_basin_hopping_result(self):
+        """When basin hopping returns a result distinct from LM-1, the final fit result carries the post-basin parameters."""
+        # Synthetic post-basin density chosen well above _TRUE_DENSITY_CM3=5.0
+        # so the round-tripped density is unambiguously the patched value.
+        # Velocity is far from the truth bulk velocity for the same reason.
+        post_basin_density = 50.0
+        post_basin_velocity = np.array([-321.0, 0.0, 0.0])
+        post_basin_temperature = 2.5e5
+
+        post_basin_result = OptimizeSolarWindParamsResult(
+            sw_params=SolarWindParams(
+                density=post_basin_density,
+                bulk_velocity_rtn=post_basin_velocity,
+                temperature=post_basin_temperature,
+                mass=PROTON_MASS_KG,
+            ),
+            residuals=np.zeros(_N_BINS_PER_SWEEP * _N_SWEEPS),
+            jacobian=np.zeros(
+                (_N_BINS_PER_SWEEP * _N_SWEEPS, N_STATE)
+            ),
+            success=True,
+        )
+
+        with patch(
+            "imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_model.escape_local_minimum",
+            return_value=post_basin_result,
+        ):
+            result = fit_solar_wind_proton_model(self.fit_ctx)
+
+        # density alone is decisive: it, temperature, and bulk_velocity_rtn
+        # propagate together from the same OptimizeSolarWindParamsResult.
+        self.assertAlmostEqual(
+            result.density.nominal_value, post_basin_density
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/science/solar_wind/proton/test_initial_guess.py b/tests/swapi/l3a/science/solar_wind/proton/test_initial_guess.py
new file mode 100644
index 000000000..83af27448
--- /dev/null
+++ b/tests/swapi/l3a/science/solar_wind/proton/test_initial_guess.py
@@ -0,0 +1,260 @@
+import unittest
+from unittest.mock import patch
+
+import numpy as np
+
+from imap_l3_processing.constants import (
+    METERS_PER_KILOMETER,
+    PROTON_CHARGE_COULOMBS,
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    build_solar_wind_fit_context,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    model_solar_wind_ideal_coincidence_rates,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.initial_guess import (
+    INITIAL_TEMPERATURE_FLOOR_K,
+    calculate_initial_guess,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    SolarWindParams,
+)
+from imap_l3_processing.swapi.l3a.utils import optimal_density_scale
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+from tests.swapi._helpers import load_swapi_response
+
+
+# RTN → SWAPI rotation. Body +Y (the SWAPI boresight / spin axis) in RTN is
+# column 1 of the transpose, i.e. -R̂_RTN. The solar wind direction (anti-
+# parallel to the spin axis) is therefore +R̂.
+_R_BASE_RTN_TO_SWAPI = np.array(
+    [[0.0, 1.0, 0.0], [-1.0, 0.0, 0.0], [0.0, 0.0, 1.0]]
+)
+
+
+def _esa_voltage_for_proton_speed(speed_km_s: float) -> float:
+    """Inverse of `esa_voltage_to_proton_speed` — pick the ESA voltage whose
+    central proton speed is exactly `speed_km_s`. Used to place the count-rate
+    peak at a known voltage."""
+    return float(
+        PROTON_MASS_KG
+        * (speed_km_s * METERS_PER_KILOMETER) ** 2
+        / (2 * SWAPI_K_FACTOR * PROTON_CHARGE_COULOMBS)
+    )
+
+
+def _spin_rotation_matrices(n: int) -> np.ndarray:
+    """SWAPI→RTN rotation matrices for `n` consecutive bins. Body +Y (the
+    SWAPI boresight / spin axis) lies along -R̂_RTN on every bin, so the
+    chunk-mean spin axis is exactly -R̂."""
+    return np.tile(_R_BASE_RTN_TO_SWAPI.T, (n, 1, 1))
+
+
+def _build_proton_ctx(count_rate: np.ndarray, esa_voltage: np.ndarray):
+    """Build a real proton-species `SolarWindFitContext` from the shipped CSVs.
+
+    `count_rate`, `esa_voltage`, and the rotation matrices are all (N,)/(N,3,3)
+    aligned in the standard L2 layout — one entry per bin, multiple sweeps
+    flattened into a single axis."""
+    response = load_swapi_response(warm_cache_voltages=esa_voltage)
+    ctx = build_solar_wind_fit_context(
+        count_rate=count_rate,
+        esa_voltage=esa_voltage,
+        swapi_response=response,
+        central_effective_area_scale=1.0,
+        rotation_matrices=_spin_rotation_matrices(len(esa_voltage)),
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    )
+    return ctx
+
+
+def _make_synthetic_ctx_at_known_truth(truth: SolarWindParams,
+                                       n_bins: int = 71):
+    """Build a context whose `count_rate` array is the noiseless ideal forward
+    model evaluated at `truth`."""
+    bulk_speed = float(np.linalg.norm(truth.bulk_velocity_rtn))
+    # Wide enough to bracket ±5σ at T=1e5 K, narrow enough to keep all bins
+    # on-instrument.
+    speed_grid = np.linspace(0.4 * bulk_speed, 1.6 * bulk_speed, n_bins)
+    voltages = np.array(
+        [_esa_voltage_for_proton_speed(s) for s in speed_grid]
+    )
+
+    # Build a placeholder ctx, evaluate the forward model at `truth` to get
+    # ideal rates, then build the production ctx with those rates as
+    # `count_rate`.
+    placeholder = _build_proton_ctx(np.ones_like(voltages), voltages)
+    ideal_rates, _ = model_solar_wind_ideal_coincidence_rates(truth, placeholder)
+    return _build_proton_ctx(ideal_rates, voltages)
+
+
+class TestCalculateInitialGuessSeeds(unittest.TestCase):
+    """Tests for `calculate_initial_guess` — doc-specified seed construction, with the Gaussian refiner patched so seeds passed to it are observable directly."""
+
+    def _ctx_with_peak_at(self, peak_speed_kms: float):
+        """Build a context whose count-rate spectrum has its maximum at a
+        bin whose ESA voltage corresponds to `peak_speed_kms`. The shape of
+        the spectrum doesn't matter — we patch out the refiner."""
+        speed_grid = np.linspace(0.4 * peak_speed_kms, 1.6 * peak_speed_kms,
+                                 31)
+        voltages = np.array(
+            [_esa_voltage_for_proton_speed(s) for s in speed_grid]
+        )
+        peak_idx = len(speed_grid) // 2  # middle bin
+        voltages[peak_idx] = _esa_voltage_for_proton_speed(peak_speed_kms)
+        count_rate = np.linspace(0.1, 0.5, len(voltages))
+        count_rate[peak_idx] = 100.0  # Unambiguous global maximum.
+        return _build_proton_ctx(count_rate, voltages)
+
+    def test_bulk_speed_seed_is_proton_speed_at_peak_voltage(self):
+        """A spectrum whose unambiguous maximum sits at the ESA voltage for 480 km/s passes 480 km/s as the bulk-speed seed into the Gaussian refiner."""
+        peak_speed = 480.0
+        ctx = self._ctx_with_peak_at(peak_speed)
+        with patch(
+            "imap_l3_processing.swapi.l3a.science.solar_wind.proton."
+            "initial_guess._gaussian_refine_bulk_speed_and_temperature",
+            return_value=(peak_speed, 1e5),
+        ) as patched_refine:
+            calculate_initial_guess(ctx)
+
+        # Args: (speed, count_rate, bulk_speed_seed, temperature_seed, mass_kg)
+        args = patched_refine.call_args.args
+        bulk_speed_seed_arg = args[2]
+        np.testing.assert_allclose(bulk_speed_seed_arg, peak_speed, rtol=1e-12)
+
+    def test_temperature_seed_uses_documented_speed_squared_formula(self):
+        """With a peak bulk speed well above the floor, the temperature seed handed to the refiner is exactly 60_000·(v/400)² K."""
+        peak_speed = 480.0
+        ctx = self._ctx_with_peak_at(peak_speed)
+        with patch(
+            "imap_l3_processing.swapi.l3a.science.solar_wind.proton."
+            "initial_guess._gaussian_refine_bulk_speed_and_temperature",
+            return_value=(peak_speed, 1e5),
+        ) as patched_refine:
+            calculate_initial_guess(ctx)
+
+        temperature_seed_arg = patched_refine.call_args.args[3]
+        expected_temperature = 60_000.0 * (peak_speed / 400.0) ** 2
+        np.testing.assert_allclose(temperature_seed_arg, expected_temperature,
+                                   rtol=1e-12)
+
+    def test_temperature_seed_floors_at_one_ev(self):
+        """At a low peak speed where 60_000·(v/400)² is below 1 eV, the temperature seed handed to the refiner is clamped to `INITIAL_TEMPERATURE_FLOOR_K`."""
+        peak_speed = 100.0
+        ctx = self._ctx_with_peak_at(peak_speed)
+        with patch(
+            "imap_l3_processing.swapi.l3a.science.solar_wind.proton."
+            "initial_guess._gaussian_refine_bulk_speed_and_temperature",
+            return_value=(peak_speed, INITIAL_TEMPERATURE_FLOOR_K),
+        ) as patched_refine:
+            calculate_initial_guess(ctx)
+
+        temperature_seed_arg = patched_refine.call_args.args[3]
+        self.assertEqual(temperature_seed_arg, INITIAL_TEMPERATURE_FLOOR_K)
+
+
+class TestCalculateInitialGuessDirection(unittest.TestCase):
+    """Tests for `calculate_initial_guess` — chunk-mean spin-axis direction handling for the returned `bulk_velocity_rtn`."""
+
+    def test_initial_velocity_is_anti_parallel_to_spin_axis(self):
+        """With every rotation matrix aligning body +Y to -R̂, the returned bulk velocity points along +R̂ — the negation of the chunk-mean spin axis."""
+        speed_grid = np.linspace(300.0, 600.0, 31)
+        voltages = np.array(
+            [_esa_voltage_for_proton_speed(s) for s in speed_grid]
+        )
+        count_rate = np.linspace(0.1, 0.5, len(voltages))
+        peak_idx = len(speed_grid) // 2  # middle bin
+        count_rate[peak_idx] = 100.0
+        ctx = _build_proton_ctx(count_rate, voltages)
+
+        # `_spin_rotation_matrices` aligns body +Y to -R̂ on every bin, so
+        # the chunk-mean spin axis is exactly -R̂.
+        expected_axis = np.array([-1.0, 0.0, 0.0])
+
+        guess = calculate_initial_guess(ctx)
+        v_unit = guess.bulk_velocity_rtn / np.linalg.norm(
+            guess.bulk_velocity_rtn
+        )
+        np.testing.assert_allclose(v_unit, -expected_axis, atol=1e-12)
+
+    def test_velocity_magnitude_matches_truth_bulk_speed(self):
+        """On a noiseless forward-model spectrum at 450 km/s the unmocked Gaussian refine recovers the truth, so the returned `|bulk_velocity_rtn|` matches the truth bulk speed to ~2%."""
+        truth_bulk_speed = 450.0
+        truth = SolarWindParams(
+            density=5.0,
+            bulk_velocity_rtn=np.array([truth_bulk_speed, 0.0, 0.0]),
+            temperature=1.0e5,
+            mass=PROTON_MASS_KG,
+        )
+        ctx = _make_synthetic_ctx_at_known_truth(truth)
+
+        guess = calculate_initial_guess(ctx)
+        np.testing.assert_allclose(
+            np.linalg.norm(guess.bulk_velocity_rtn),
+            truth_bulk_speed,
+            rtol=2e-2,
+        )
+
+
+class TestCalculateInitialGuessDensity(unittest.TestCase):
+    """Tests for `calculate_initial_guess` — the returned density is the optimal scale of the unit-density forward model against the observed count rates."""
+
+    def test_density_is_optimal_scale_of_unit_density_forward_model(self):
+        """Re-running the unit-density forward model at the guess's own velocity/temperature and feeding it through `optimal_density_scale` reproduces the guess density exactly."""
+        truth = SolarWindParams(
+            density=4.2,
+            bulk_velocity_rtn=np.array([470.0, 0.0, 0.0]),
+            temperature=1.2e5,
+            mass=PROTON_MASS_KG,
+        )
+        ctx = _make_synthetic_ctx_at_known_truth(truth)
+
+        guess = calculate_initial_guess(ctx)
+
+        # Reproduce the function's density step independently: build a
+        # unit-density forward-model evaluation at the *same* bulk-velocity
+        # direction and temperature the function uses, then call
+        # `optimal_density_scale`. The result must match `guess.density`.
+        unit_density_params = SolarWindParams(
+            density=1.0,
+            bulk_velocity_rtn=guess.bulk_velocity_rtn,
+            temperature=guess.temperature,
+            mass=ctx.mass_kg,
+        )
+        unit_rates, _ = model_solar_wind_ideal_coincidence_rates(
+            unit_density_params, ctx
+        )
+        expected_density = optimal_density_scale(unit_rates, ctx.count_rate)
+        np.testing.assert_allclose(guess.density, expected_density, rtol=1e-10)
+
+
+class TestCalculateInitialGuessRefinerFailure(unittest.TestCase):
+    """Tests for `calculate_initial_guess` — pathological spectra that the Gaussian refiner cannot fit surface as a wrapped `RuntimeError`, with the offending peak-bin speed and seed temperature included in the message and the underlying `scipy` error chained as `__cause__`."""
+
+    def test_wraps_runtime_error_on_pathological_edge_spike_spectrum(self):
+        """A count-rate spectrum with an extreme isolated spike at the lowest-speed bin (1e10 surrounded by 1e-6) drives `curve_fit` past its `maxfev` budget; the refiner re-raises the resulting `RuntimeError` with a wrapped message that names the peak-bin seed, and the scipy error is preserved on `__cause__`."""
+        peak_speed = 250.0
+        speed_grid = np.linspace(peak_speed, peak_speed + 600.0, 71)
+        voltages = np.array(
+            [_esa_voltage_for_proton_speed(s) for s in speed_grid]
+        )
+        count_rate = np.full(len(voltages), 1e-6)
+        count_rate[0] = 1.0e10
+        ctx = _build_proton_ctx(count_rate, voltages)
+
+        with self.assertRaises(RuntimeError) as raise_context:
+            calculate_initial_guess(ctx)
+
+        message = str(raise_context.exception)
+        self.assertIn("Initial-guess Gaussian fit failed", message)
+        self.assertIn(f"{peak_speed:.1f}", message)
+        self.assertIsInstance(raise_context.exception.__cause__, RuntimeError)
+        self.assertIn("maxfev", str(raise_context.exception.__cause__))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/science/solar_wind/test_azimuthal_regions.py b/tests/swapi/l3a/science/solar_wind/test_azimuthal_regions.py
new file mode 100644
index 000000000..fabf879de
--- /dev/null
+++ b/tests/swapi/l3a/science/solar_wind/test_azimuthal_regions.py
@@ -0,0 +1,40 @@
+import unittest
+
+from imap_l3_processing.swapi.l3a.science.solar_wind.azimuthal_regions import (
+    REGION_OPEN_APERTURE_NEG,
+    REGION_OPEN_APERTURE_POS,
+    REGION_SUNGLASSES,
+    Region,
+)
+
+
+class TestRegionConstants(unittest.TestCase):
+    """Tests for the `REGION_SUNGLASSES`, `REGION_OPEN_APERTURE_NEG`, and `REGION_OPEN_APERTURE_POS` constants."""
+
+    def test_sunglasses_region_flags_and_azimuth_sign(self):
+        """The SG constant reports sunglasses=True, open-aperture=False, and azimuth_sign=0 since it spans both halves of the boresight band."""
+        self.assertTrue(REGION_SUNGLASSES.is_sunglasses)
+        self.assertFalse(REGION_SUNGLASSES.is_open_aperture)
+        self.assertEqual(REGION_SUNGLASSES.azimuth_sign, 0)
+
+    def test_open_aperture_negative_wing_flags_and_sign(self):
+        """The OA- wing constant reports open-aperture=True and azimuth_sign=-1 to select the negative-azimuth half."""
+        self.assertFalse(REGION_OPEN_APERTURE_NEG.is_sunglasses)
+        self.assertTrue(REGION_OPEN_APERTURE_NEG.is_open_aperture)
+        self.assertEqual(REGION_OPEN_APERTURE_NEG.azimuth_sign, -1)
+
+    def test_open_aperture_positive_wing_flags_and_sign(self):
+        """The OA+ wing constant reports open-aperture=True and azimuth_sign=+1 to select the positive-azimuth half."""
+        self.assertFalse(REGION_OPEN_APERTURE_POS.is_sunglasses)
+        self.assertTrue(REGION_OPEN_APERTURE_POS.is_open_aperture)
+        self.assertEqual(REGION_OPEN_APERTURE_POS.azimuth_sign, +1)
+
+    def test_region_namedtuple_field_order_is_stable(self):
+        """The Region NamedTuple field order is locked so positional unpacking in numba forward-model loops keeps working."""
+        self.assertEqual(
+            Region._fields, ("is_sunglasses", "is_open_aperture", "azimuth_sign")
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/science/solar_wind/test_fit_context.py b/tests/swapi/l3a/science/solar_wind/test_fit_context.py
new file mode 100644
index 000000000..96bf5f816
--- /dev/null
+++ b/tests/swapi/l3a/science/solar_wind/test_fit_context.py
@@ -0,0 +1,190 @@
+import unittest
+from unittest.mock import MagicMock
+
+import numpy as np
+
+from imap_l3_processing.constants import PROTON_MASS_KG
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    SolarWindFitContext,
+    build_solar_wind_fit_context,
+)
+from imap_l3_processing.swapi.response.azimuthal_transmission import (
+    AzimuthalTransmissionGrid,
+)
+from imap_l3_processing.swapi.response.swapi_response import ResponseGrid
+
+
+def _swapi_response_returning_per_voltage_response_grid():
+    """Mock SwapiResponse whose `get_response_grid(v, ...)` returns a real
+    `ResponseGrid` NamedTuple tagged with the voltage in its `central_speed`
+    field — lets tests assert on order and per-sweep identity without fighting
+    numba's typed-list element-type inference (which rejects MagicMock)."""
+    response = MagicMock()
+
+    def _build_response_grid(voltage, *args, **kwargs):
+        # Tag the voltage in `central_speed` so tests can recover which
+        # voltage produced which grid. Other fields use shape-correct
+        # placeholders that satisfy numba's type system.
+        return ResponseGrid(
+            sg_passband=None,
+            oa_passband=None,
+            central_speed=float(voltage),
+            central_effective_area=0.0,
+            azimuthal_transmission=AzimuthalTransmissionGrid(
+                values=np.zeros(1), spacing=0.1
+            ),
+        )
+
+    response.get_response_grid.side_effect = _build_response_grid
+    return response
+
+
+class TestSolarWindFitContextSubset(unittest.TestCase):
+    """Tests for `SolarWindFitContext.subset` — selects per-sweep arrays by index while leaving scalar `mass_kg` intact."""
+
+    def setUp(self):
+        # 4 sweeps with distinguishable per-sweep values. The "rotation
+        # matrices" here are not real rotations (det ≠ 1) — they're just
+        # arrays distinguishable by their first entry, used to verify the
+        # subset operation indexes correctly.
+        self.full_ctx = SolarWindFitContext(
+            count_rate=np.array([10.0, 20.0, 30.0, 40.0]),
+            esa_voltage=np.array([100.0, 200.0, 300.0, 400.0]),
+            response_grids=["grid_0", "grid_1", "grid_2", "grid_3"],
+            rotation_matrices=np.stack(
+                [np.eye(3) * (i + 1) for i in range(4)]
+            ),
+            mass_kg=PROTON_MASS_KG,
+        )
+
+    def test_subset_with_all_indices_returns_equivalent_context(self):
+        """Subsetting with the full index range reproduces every per-sweep array unchanged."""
+        kept = self.full_ctx.subset(np.array([0, 1, 2, 3]))
+        np.testing.assert_array_equal(kept.count_rate, self.full_ctx.count_rate)
+        np.testing.assert_array_equal(kept.esa_voltage, self.full_ctx.esa_voltage)
+        # `subset` rebuilds response_grids as a numba.typed.List; cast to a
+        # plain list for value-level comparison.
+        self.assertEqual(list(kept.response_grids), self.full_ctx.response_grids)
+        np.testing.assert_array_equal(
+            kept.rotation_matrices, self.full_ctx.rotation_matrices
+        )
+
+    def test_subset_picks_per_sweep_arrays_at_the_given_indices(self):
+        """Selecting indices [1, 3] yields a context whose count_rate, voltage, response_grids, and rotation_matrices are all picked at those positions."""
+        kept = self.full_ctx.subset(np.array([1, 3]))
+        np.testing.assert_array_equal(kept.count_rate, [20.0, 40.0])
+        np.testing.assert_array_equal(kept.esa_voltage, [200.0, 400.0])
+        self.assertEqual(list(kept.response_grids), ["grid_1", "grid_3"])
+        np.testing.assert_array_equal(
+            kept.rotation_matrices,
+            self.full_ctx.rotation_matrices[[1, 3]],
+        )
+
+    def test_subset_preserves_mass(self):
+        """Subsetting to a single sweep leaves the scalar `mass_kg` field untouched."""
+        kept = self.full_ctx.subset(np.array([0]))
+        self.assertEqual(kept.mass_kg, PROTON_MASS_KG)
+
+
+class TestBuildSolarWindFitContext(unittest.TestCase):
+    """Tests for `build_solar_wind_fit_context` — filters invalid sweeps from per-sweep arrays in lockstep and materializes a per-sweep `ResponseGrid`."""
+
+    def _call_factory(self, *, count_rate, esa_voltage, rotation_matrices=None):
+        response = _swapi_response_returning_per_voltage_response_grid()
+        if rotation_matrices is None:
+            rotation_matrices = np.stack([np.eye(3)] * len(count_rate))
+        ctx = build_solar_wind_fit_context(
+            count_rate=count_rate,
+            esa_voltage=esa_voltage,
+            swapi_response=response,
+            central_effective_area_scale=1.0,
+            rotation_matrices=rotation_matrices,
+            mass_kg=PROTON_MASS_KG,
+            mass_per_charge_m_p_per_e=1.0,
+        )
+        return ctx, response
+
+    def test_keeps_all_sweeps_when_voltages_are_finite_and_positive(self):
+        """When every voltage is finite and positive, no sweeps are dropped and one ResponseGrid is built per sweep."""
+        count_rate = np.array([10.0, 20.0, 30.0])
+        esa_voltage = np.array([100.0, 200.0, 300.0])
+        ctx, response = self._call_factory(
+            count_rate=count_rate, esa_voltage=esa_voltage
+        )
+        np.testing.assert_array_equal(ctx.count_rate, count_rate)
+        np.testing.assert_array_equal(ctx.esa_voltage, esa_voltage)
+        self.assertEqual(len(ctx.response_grids), 3)
+
+    def test_drops_sweeps_with_zero_or_negative_voltage(self):
+        """Sweeps with voltage == 0 or voltage < 0 are filtered out of count_rate and esa_voltage together."""
+        ctx, _ = self._call_factory(
+            count_rate=np.array([10.0, 20.0, 30.0, 40.0]),
+            esa_voltage=np.array([100.0, 0.0, -50.0, 400.0]),
+        )
+        np.testing.assert_array_equal(ctx.count_rate, [10.0, 40.0])
+        np.testing.assert_array_equal(ctx.esa_voltage, [100.0, 400.0])
+
+    def test_drops_sweeps_with_non_finite_voltage(self):
+        """NaN and inf voltages are treated as invalid and dropped alongside their count_rate entries."""
+        ctx, _ = self._call_factory(
+            count_rate=np.array([10.0, 20.0, 30.0]),
+            esa_voltage=np.array([100.0, np.nan, np.inf]),
+        )
+        np.testing.assert_array_equal(ctx.count_rate, [10.0])
+        np.testing.assert_array_equal(ctx.esa_voltage, [100.0])
+
+    def test_filters_rotation_matrices_in_lockstep_with_voltages(self):
+        """When a middle sweep is dropped for a zero voltage, its rotation matrix is dropped at the same index so downstream geometry stays aligned."""
+        rotations = np.stack([np.eye(3) * (i + 1) for i in range(3)])
+        ctx, _ = self._call_factory(
+            count_rate=np.array([10.0, 20.0, 30.0]),
+            esa_voltage=np.array([100.0, 0.0, 300.0]),
+            rotation_matrices=rotations,
+        )
+        np.testing.assert_array_equal(
+            ctx.rotation_matrices, rotations[[0, 2]]
+        )
+
+    def test_creates_response_grid_for_each_kept_voltage(self):
+        """The factory calls `get_response_grid` once per kept voltage and stores the resulting grids in the same order as the input voltages."""
+        count_rate = np.array([10.0, 20.0])
+        esa_voltage = np.array([100.0, 200.0])
+        ctx, response = self._call_factory(
+            count_rate=count_rate, esa_voltage=esa_voltage
+        )
+        self.assertEqual(response.get_response_grid.call_count, 2)
+        self.assertEqual(ctx.response_grids[0].central_speed, 100.0)
+        self.assertEqual(ctx.response_grids[1].central_speed, 200.0)
+
+    def test_filters_response_grids_in_lockstep_with_voltages(self):
+        """When a sweep with a NaN voltage is dropped, no ResponseGrid is built for it and the kept grids stay aligned with the kept voltages."""
+        ctx, _ = self._call_factory(
+            count_rate=np.array([10.0, 20.0, 30.0]),
+            esa_voltage=np.array([100.0, np.nan, 300.0]),
+        )
+        self.assertEqual(len(ctx.response_grids), 2)
+        self.assertEqual(ctx.response_grids[0].central_speed, 100.0)
+        self.assertEqual(ctx.response_grids[1].central_speed, 300.0)
+
+    def test_passes_species_and_efficiency_args_to_create_response_grid(self):
+        """The mass-per-charge and central-effective-area scale supplied to the factory are forwarded into the `get_response_grid` call for each sweep."""
+        response = _swapi_response_returning_per_voltage_response_grid()
+        build_solar_wind_fit_context(
+            count_rate=np.array([10.0]),
+            esa_voltage=np.array([100.0]),
+            swapi_response=response,
+            central_effective_area_scale=0.42,
+            rotation_matrices=np.eye(3)[np.newaxis],
+            mass_kg=PROTON_MASS_KG,
+            mass_per_charge_m_p_per_e=2.0,  # alpha-like mass-per-charge
+        )
+        self.assertEqual(response.get_response_grid.call_count, 1)
+        call = response.get_response_grid.call_args
+        all_args = list(call.args) + list(call.kwargs.values())
+        self.assertIn(100.0, all_args)
+        self.assertIn(2.0, all_args)
+        self.assertIn(0.42, all_args)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/science/solar_wind/test_forward_model.py b/tests/swapi/l3a/science/solar_wind/test_forward_model.py
new file mode 100644
index 000000000..0c986f99c
--- /dev/null
+++ b/tests/swapi/l3a/science/solar_wind/test_forward_model.py
@@ -0,0 +1,364 @@
+import math
+import unittest
+
+import numpy as np
+
+from imap_l3_processing.constants import (
+    PROTON_CHARGE_COULOMBS,
+    PROTON_MASS_KG,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    build_solar_wind_fit_context,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    calculate_integral,
+    model_solar_wind_ideal_coincidence_rates,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    LOG_DENSITY_IDX,
+    LOG_TEMPERATURE_IDX,
+    N_STATE,
+    SolarWindParams,
+    VELOCITY_SLICE,
+)
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
+from tests.swapi._helpers import proton_params
+from tests.test_helpers import get_test_instrument_team_data_path
+
+
+# Reference bulk speed for the slow-wind fixture state shared by every test
+# in this module (5 cm⁻³, 100 kK protons under the default `proton_params`).
+_REF_BULK_SPEED_KM_S = 450.0
+
+# Off-axis bulk velocity used for the velocity-component Jacobian tests.
+# Adding small components in R and N breaks the symmetry that makes vR/vN
+# Jacobian entries near zero, so finite-difference noise doesn't dominate.
+_OFF_AXIS_BULK_VELOCITY_RTN = np.array([30.0, -_REF_BULK_SPEED_KM_S, 20.0])
+
+# Index aliases for the RTN velocity components inside the LM state vector.
+_VELOCITY_R_IDX, _VELOCITY_T_IDX, _VELOCITY_N_IDX = (
+    VELOCITY_SLICE.start,
+    VELOCITY_SLICE.start + 1,
+    VELOCITY_SLICE.start + 2,
+)
+
+
+def _esa_voltage_at_speed(speed_km_s: float) -> float:
+    """ESA voltage (V) whose central proton speed equals `speed_km_s`. The
+    relation is `V = ½ m_p v² / (e · k_SWAPI)`."""
+    return (
+        0.5
+        * PROTON_MASS_KG
+        * (speed_km_s * 1e3) ** 2
+        / PROTON_CHARGE_COULOMBS
+        / SWAPI_K_FACTOR
+    )
+
+
+# Putting the bulk speed at the passband center maximizes the rate and
+# minimizes truncation effects in the quadrature.
+_ESA_VOLTAGE_AT_REF_SPEED_V = _esa_voltage_at_speed(_REF_BULK_SPEED_KM_S)
+
+# Numerical-differentiation parameters. `1e-5` is small enough that truncation
+# from finite differences is well below the GL quadrature noise floor for the
+# scales involved (≈10⁴ Hz rates, ≈10² km/s velocities) but large enough that
+# round-off error stays well below 1%.
+_FD_RELATIVE_STEP = 1e-5
+
+# Tolerances for analytic-vs-finite-difference Jacobian comparison. The Jacobian
+# is exact in the continuum but is evaluated with the same Gauss-Legendre
+# quadrature as the rate; the small mismatch comes from the dynamic-quadrature
+# limits shifting (slightly) when the state is perturbed for finite differences,
+# which moves the GL nodes off the exact integrand peaks. Measured worst case
+# on this fixture (off-axis bulk, 100 kK, 450 km/s) is ~2.74% on the vR column;
+# 5% leaves headroom for the same machinery to stay green on adjacent fixtures
+# while still flagging an analytic-derivative bug (sign flip or factor-of-2,
+# which would shift the column by ~100% or ~50%). To remeasure: evaluate
+# `model_solar_wind_ideal_coincidence_rates` at the test fixture state and
+# compare each column against `_finite_difference_jacobian_column`.
+_JACOBIAN_RTOL = 0.05
+_JACOBIAN_ATOL = 1e-3
+
+
+
+
+def _build_fit_context_for_voltages(
+    response: SwapiResponse,
+    esa_voltages: np.ndarray,
+    rotation_matrices: np.ndarray = None,
+):
+    """Wrap `build_solar_wind_fit_context` with sensible defaults for these
+    tests: identity rotations and dummy count rates."""
+    voltages_array = np.asarray(esa_voltages, dtype=float)
+    if rotation_matrices is None:
+        rotation_matrices = np.stack([np.eye(3)] * len(voltages_array))
+    response.warm_cache(voltages_array)
+    return build_solar_wind_fit_context(
+        count_rate=np.full(len(voltages_array), 100.0),
+        esa_voltage=voltages_array,
+        swapi_response=response,
+        central_effective_area_scale=1.0,
+        rotation_matrices=rotation_matrices,
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=1.0,
+    )
+
+
+class _ForwardModelFixture(unittest.TestCase):
+    """Loads the SwapiResponse once per class — the CSV parsing is not free
+    and none of these tests mutate the response."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.response = SwapiResponse.from_files(
+            get_test_instrument_team_data_path(
+                "swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv"
+            ),
+            get_test_instrument_team_data_path(
+                "swapi/imap_swapi_central-effective-area_20260425_v001.csv"
+            ),
+            get_test_instrument_team_data_path(
+                "swapi/imap_swapi_passband-fit-coefficients_20260425_v001.csv"
+            ),
+        )
+
+
+class TestModelSolarWindIdealCoincidenceRatesShape(_ForwardModelFixture):
+    """Tests for `model_solar_wind_ideal_coincidence_rates`, output-shape contract."""
+
+    def test_returns_one_rate_and_one_jacobian_row_per_sweep(self):
+        """Given three sweeps spanning the passband-center voltage, the model returns a length-3 rate vector and a (3, N_STATE) Jacobian."""
+        ctx = _build_fit_context_for_voltages(
+            self.response,
+            np.array(
+                [
+                    _ESA_VOLTAGE_AT_REF_SPEED_V * 0.95,
+                    _ESA_VOLTAGE_AT_REF_SPEED_V,
+                    _ESA_VOLTAGE_AT_REF_SPEED_V * 1.05,
+                ]
+            ),
+        )
+        rates, jacobian = model_solar_wind_ideal_coincidence_rates(
+            proton_params(), ctx
+        )
+        self.assertEqual(rates.shape, (3,))
+        self.assertEqual(jacobian.shape, (3, N_STATE))
+
+
+class TestCalculateIntegralRateBehavior(_ForwardModelFixture):
+    """Tests for `calculate_integral`, rate-value behavior (Jacobian asserted elsewhere)."""
+
+    def setUp(self):
+        self.ctx = _build_fit_context_for_voltages(
+            self.response, np.array([_ESA_VOLTAGE_AT_REF_SPEED_V])
+        )
+        self.response_grid = self.ctx.response_grids[0]
+        self.rotation_matrix = self.ctx.rotation_matrices[0]
+
+    def test_rate_is_positive_when_bulk_velocity_aligns_with_boresight(self):
+        """With bulk speed at the passband center and the flow aimed along boresight, the predicted rate is strictly positive."""
+        rate, _ = calculate_integral(
+            proton_params(), self.response_grid, self.rotation_matrix
+        )
+        self.assertGreater(rate, 0.0)
+
+    def test_rate_is_linear_in_density(self):
+        """Doubling the proton density at fixed temperature and geometry doubles the predicted rate exactly, since the rate is linear in `n`."""
+        rate_at_5, _ = calculate_integral(
+            proton_params(density=5.0),
+            self.response_grid,
+            self.rotation_matrix,
+        )
+        rate_at_10, _ = calculate_integral(
+            proton_params(density=10.0),
+            self.response_grid,
+            self.rotation_matrix,
+        )
+        np.testing.assert_allclose(rate_at_10, 2.0 * rate_at_5, rtol=1e-12)
+
+    def test_returns_zero_rate_and_jacobian_when_bulk_speed_misses_passband_above(self):
+        """When the bulk speed (2000 km/s) sits hundreds of sigma above the passband at this voltage, the model short-circuits to rate=0 and a zero Jacobian row."""
+        very_fast = proton_params(velocity_rtn=np.array([0.0, -2000.0, 0.0]))
+        rate, jacobian_row = calculate_integral(
+            very_fast, self.response_grid, self.rotation_matrix
+        )
+        self.assertEqual(rate, 0.0)
+        np.testing.assert_array_equal(jacobian_row, np.zeros(N_STATE))
+
+    def test_returns_zero_rate_and_jacobian_when_bulk_speed_misses_passband_below(self):
+        """Mirror of the above on the low side: a 50 km/s bulk sits far below the passband at this voltage, so rate=0 and the Jacobian row is zero."""
+        very_slow = proton_params(velocity_rtn=np.array([0.0, -50.0, 0.0]))
+        rate, jacobian_row = calculate_integral(
+            very_slow, self.response_grid, self.rotation_matrix
+        )
+        self.assertEqual(rate, 0.0)
+        np.testing.assert_array_equal(jacobian_row, np.zeros(N_STATE))
+
+
+class TestAnalyticJacobianIdentities(_ForwardModelFixture):
+    """Tests for `model_solar_wind_ideal_coincidence_rates`, exact closed-form Jacobian identities that hold independent of quadrature."""
+
+    def test_log_density_jacobian_column_equals_the_rate(self):
+        """Because the rate is linear in `n`, the analytic log-density Jacobian column equals the rate vector exactly with no quadrature slack."""
+        ctx = _build_fit_context_for_voltages(
+            self.response,
+            np.array(
+                [
+                    _ESA_VOLTAGE_AT_REF_SPEED_V * 0.95,
+                    _ESA_VOLTAGE_AT_REF_SPEED_V,
+                    _ESA_VOLTAGE_AT_REF_SPEED_V * 1.05,
+                ]
+            ),
+        )
+        rates, jacobian = model_solar_wind_ideal_coincidence_rates(
+            proton_params(velocity_rtn=_OFF_AXIS_BULK_VELOCITY_RTN),
+            ctx,
+        )
+        np.testing.assert_array_equal(jacobian[:, LOG_DENSITY_IDX], rates)
+
+
+def _rate_at_state(state: np.ndarray, ctx) -> np.ndarray:
+    """Evaluate the rate vector at a state-vector point (no Jacobian)."""
+    sw_local = SolarWindParams.from_vector(state, mass=PROTON_MASS_KG)
+    rates, _ = model_solar_wind_ideal_coincidence_rates(sw_local, ctx)
+    return rates.copy()
+
+
+def _finite_difference_jacobian_column(state: np.ndarray, ctx, j: int) -> np.ndarray:
+    """Central-difference column `j` of the Jacobian: `(C(x+ε e_j) − C(x−ε e_j)) / 2ε`."""
+    eps = _FD_RELATIVE_STEP * max(abs(state[j]), 1.0)
+    forward = state.copy()
+    forward[j] += eps
+    backward = state.copy()
+    backward[j] -= eps
+    return (_rate_at_state(forward, ctx) - _rate_at_state(backward, ctx)) / (2 * eps)
+
+
+class TestAnalyticJacobianAgainstFiniteDifferences(_ForwardModelFixture):
+    """Tests for `model_solar_wind_ideal_coincidence_rates`, analytic Jacobian columns vs. central finite differences."""
+
+    def setUp(self):
+        # Two-sweep context with a small voltage gradient so each sweep
+        # contributes a different, non-degenerate row to the Jacobian.
+        self.ctx = _build_fit_context_for_voltages(
+            self.response,
+            np.array(
+                [
+                    _ESA_VOLTAGE_AT_REF_SPEED_V,
+                    _ESA_VOLTAGE_AT_REF_SPEED_V * 1.05,
+                ]
+            ),
+        )
+        # Off-axis bulk velocity so all three velocity components produce
+        # nonzero Jacobian entries (boresight-aligned bulk has vR/vN ≈ 0
+        # by symmetry, and the FD signal-to-noise on those columns is poor).
+        self.sw = proton_params(velocity_rtn=_OFF_AXIS_BULK_VELOCITY_RTN)
+        self.state = self.sw.to_vector()
+
+    def test_log_temperature_jacobian_is_correct(self):
+        """At an off-axis bulk state, the analytic log-temperature Jacobian column matches a central finite difference of the rate to within GL quadrature noise."""
+        _, analytic_jacobian = model_solar_wind_ideal_coincidence_rates(
+            self.sw, self.ctx
+        )
+        fd_column = _finite_difference_jacobian_column(
+            self.state, self.ctx, LOG_TEMPERATURE_IDX
+        )
+        np.testing.assert_allclose(
+            analytic_jacobian[:, LOG_TEMPERATURE_IDX],
+            fd_column,
+            rtol=_JACOBIAN_RTOL,
+            atol=_JACOBIAN_ATOL,
+        )
+
+    def test_velocity_r_jacobian_is_correct(self):
+        """At an off-axis bulk state, the analytic v_R Jacobian column matches a central finite difference of the rate to within GL quadrature noise."""
+        _, analytic_jacobian = model_solar_wind_ideal_coincidence_rates(
+            self.sw, self.ctx
+        )
+        fd_column = _finite_difference_jacobian_column(
+            self.state, self.ctx, _VELOCITY_R_IDX
+        )
+        np.testing.assert_allclose(
+            analytic_jacobian[:, _VELOCITY_R_IDX],
+            fd_column,
+            rtol=_JACOBIAN_RTOL,
+            atol=_JACOBIAN_ATOL,
+        )
+
+    def test_velocity_t_jacobian_is_correct(self):
+        """At an off-axis bulk state, the analytic v_T Jacobian column matches a central finite difference of the rate to within GL quadrature noise."""
+        _, analytic_jacobian = model_solar_wind_ideal_coincidence_rates(
+            self.sw, self.ctx
+        )
+        fd_column = _finite_difference_jacobian_column(
+            self.state, self.ctx, _VELOCITY_T_IDX
+        )
+        np.testing.assert_allclose(
+            analytic_jacobian[:, _VELOCITY_T_IDX],
+            fd_column,
+            rtol=_JACOBIAN_RTOL,
+            atol=_JACOBIAN_ATOL,
+        )
+
+    def test_velocity_n_jacobian_is_correct(self):
+        """At an off-axis bulk state, the analytic v_N Jacobian column matches a central finite difference of the rate to within GL quadrature noise."""
+        _, analytic_jacobian = model_solar_wind_ideal_coincidence_rates(
+            self.sw, self.ctx
+        )
+        fd_column = _finite_difference_jacobian_column(
+            self.state, self.ctx, _VELOCITY_N_IDX
+        )
+        np.testing.assert_allclose(
+            analytic_jacobian[:, _VELOCITY_N_IDX],
+            fd_column,
+            rtol=_JACOBIAN_RTOL,
+            atol=_JACOBIAN_ATOL,
+        )
+
+
+class TestNonIdentityRotation(_ForwardModelFixture):
+    """Tests for `calculate_integral`, invariance under joint rotation of bulk velocity and SWAPI->RTN matrix."""
+
+    def test_rate_is_invariant_under_joint_rotation_of_bulk_and_response(self):
+        """Rotating both the bulk velocity (in RTN) and the SWAPI->RTN matrix by the same 30 degree rotation reproduces the identity-rotation baseline rate."""
+        baseline_ctx = _build_fit_context_for_voltages(
+            self.response, np.array([_ESA_VOLTAGE_AT_REF_SPEED_V])
+        )
+        baseline_rate, _ = calculate_integral(
+            proton_params(velocity_rtn=_OFF_AXIS_BULK_VELOCITY_RTN),
+            baseline_ctx.response_grids[0],
+            baseline_ctx.rotation_matrices[0],
+        )
+
+        # 30° rotation about RTN +R. Rotating both the bulk velocity and the
+        # instrument-to-RTN matrix by the same Q preserves the bulk direction
+        # in the instrument frame, which is all the integrand sees.
+        angle_rad = math.radians(30.0)
+        rotation_about_r = np.array(
+            [
+                [1.0, 0.0, 0.0],
+                [0.0, math.cos(angle_rad), -math.sin(angle_rad)],
+                [0.0, math.sin(angle_rad), math.cos(angle_rad)],
+            ]
+        )
+        rotated_velocity_rtn = rotation_about_r @ _OFF_AXIS_BULK_VELOCITY_RTN
+        rotated_xyz_to_rtn = rotation_about_r
+        rotated_ctx = _build_fit_context_for_voltages(
+            self.response,
+            np.array([_ESA_VOLTAGE_AT_REF_SPEED_V]),
+            rotation_matrices=np.stack([rotated_xyz_to_rtn]),
+        )
+        rotated_rate, _ = calculate_integral(
+            proton_params(velocity_rtn=rotated_velocity_rtn),
+            rotated_ctx.response_grids[0],
+            rotated_ctx.rotation_matrices[0],
+        )
+
+        # Agreement is limited only by GL quadrature node positions, which
+        # are placed on the same bulk-frame angular window in both cases.
+        np.testing.assert_allclose(rotated_rate, baseline_rate, rtol=1e-6)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/science/solar_wind/test_integration_limits.py b/tests/swapi/l3a/science/solar_wind/test_integration_limits.py
new file mode 100644
index 000000000..93fffa8db
--- /dev/null
+++ b/tests/swapi/l3a/science/solar_wind/test_integration_limits.py
@@ -0,0 +1,426 @@
+import unittest
+
+import numpy as np
+
+from imap_l3_processing.constants import PROTON_MASS_KG
+from imap_l3_processing.swapi.l3a.science.solar_wind.azimuthal_regions import (
+    REGION_OPEN_APERTURE_NEG,
+    REGION_OPEN_APERTURE_POS,
+    REGION_SUNGLASSES,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.integration_limits import (
+    SPEED_HALF_WIDTH_VTH,
+    get_angular_quadrature,
+    get_speed_quadrature,
+    speed_window_misses_passband,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    SolarWindParams,
+    bulk_speed,
+    thermal_speed,
+    thermal_speed_to_temperature,
+)
+from imap_l3_processing.swapi.response.passband_grid import (
+    interpolate_passband,
+    speed_ratio_range_at_elevation,
+)
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+from tests.swapi._helpers import (
+    NOMINAL_SWAPI_TO_RTN_ROTATION,
+    load_swapi_response,
+    proton_params,
+)
+
+# --- module-level fixtures: real instrument-team CSVs and one warmed grid ----
+
+# A 1 keV proton ESA voltage gives a comfortably typical central speed
+# (~437 km/s) and a passband elevation range that contains 0.
+_ESA_VOLTAGE = 1000.0 / SWAPI_K_FACTOR
+
+# Region azimuth bands fixed by the doc.
+_SG_AZIMUTH_LO_DEG = -20.0
+_SG_AZIMUTH_HI_DEG = +20.0
+_OA_AZIMUTH_INNER_DEG = 20.0
+_OA_AZIMUTH_OUTER_DEG = 150.0
+
+_BULK_SPEED_KM_S = 450.0
+
+
+class TestSpeedWindowMissesPassband(unittest.TestCase):
+    """Tests for `speed_window_misses_passband` — the cheap pre-check that returns True only when `bulk_speed ± k·vth` is fully disjoint from the widest possible passband speed range (`central_speed · [0.9, 1.1]`)."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.response = load_swapi_response(warm_cache_voltages=[_ESA_VOLTAGE])
+        cls.response_grid = cls.response.get_response_grid(_ESA_VOLTAGE, 1.0)
+
+    def test_returns_false_when_bulk_speed_sits_in_passband(self):
+        """A 450 km/s bulk at the 1 keV step sits well inside the passband envelope, so no miss is reported."""
+        sw = proton_params()
+        self.assertFalse(speed_window_misses_passband(sw, self.response_grid))
+
+    def test_returns_true_when_bulk_speed_above_passband(self):
+        """A 1500 km/s bulk puts the entire `bulk ± k·σ` window above the upper passband edge, so a miss is reported."""
+        sw = proton_params(velocity_rtn=(0.0, -1500.0, 0.0))
+        self.assertTrue(speed_window_misses_passband(sw, self.response_grid))
+
+    def test_returns_true_when_bulk_speed_below_passband(self):
+        """A 100 km/s bulk puts the entire `bulk ± k·σ` window below the lower passband edge, so a miss is reported."""
+        sw = proton_params(velocity_rtn=(0.0, -100.0, 0.0))
+        self.assertTrue(speed_window_misses_passband(sw, self.response_grid))
+
+    def test_window_overlaps_passband_when_k_sigma_exceeds_offset_above_edge(self):
+        """With bulk 30 km/s above the passband envelope edge and `k·σ = 50 km/s`, the window still reaches into the passband and no miss is reported — pinning `SPEED_HALF_WIDTH_VTH` as the scaling constant when paired with the detach test below."""
+        v_passband_hi = self.response_grid.central_speed * 1.1
+        bulk = v_passband_hi + 30.0
+        sigma_overlap = 50.0 / SPEED_HALF_WIDTH_VTH
+        T_overlap = thermal_speed_to_temperature(sigma_overlap, PROTON_MASS_KG)
+        sw_overlap = proton_params(
+            velocity_rtn=(0.0, -bulk, 0.0), temperature=T_overlap
+        )
+        self.assertFalse(speed_window_misses_passband(sw_overlap, self.response_grid))
+
+    def test_window_detaches_from_passband_when_k_sigma_falls_below_offset(self):
+        """With the same 30 km/s offset above the edge but `k·σ = 5 km/s`, the window stays clear of the passband and a miss is reported — the companion case to the overlap test."""
+        v_passband_hi = self.response_grid.central_speed * 1.1
+        bulk = v_passband_hi + 30.0
+        sigma_detached = 5.0 / SPEED_HALF_WIDTH_VTH
+        T_detached = thermal_speed_to_temperature(sigma_detached, PROTON_MASS_KG)
+        sw_detached = proton_params(
+            velocity_rtn=(0.0, -bulk, 0.0), temperature=T_detached
+        )
+        self.assertTrue(speed_window_misses_passband(sw_detached, self.response_grid))
+
+
+class TestGetAngularQuadratureSunglassesRegion(unittest.TestCase):
+    """Tests for `get_angular_quadrature` on the SG region — the 21×21 GL rectangle clamped to the SG elevation range and `[-20°, +20°]` azimuth band."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.response = load_swapi_response(warm_cache_voltages=[_ESA_VOLTAGE])
+        cls.response_grid = cls.response.get_response_grid(_ESA_VOLTAGE, 1.0)
+        cls.identity = NOMINAL_SWAPI_TO_RTN_ROTATION
+
+    def test_sg_region_is_active_for_sun_pointed_bulk(self):
+        """A boresight-aligned bulk with a narrow Maxwellian falls inside the SG band, so the region is active and a quadrature is returned."""
+        sw = proton_params()
+        skip, quadrature = get_angular_quadrature(
+            sw, self.response_grid, REGION_SUNGLASSES, self.identity, 0.0
+        )
+        self.assertFalse(skip)
+        self.assertIsNotNone(quadrature)
+
+    def test_sg_azimuth_window_is_inside_minus_20_to_plus_20_degrees(self):
+        """For a boresight-aligned bulk, every azimuth GL node lies inside the SG `[-20°, +20°]` band."""
+        sw = proton_params()
+        _, quadrature = get_angular_quadrature(
+            sw, self.response_grid, REGION_SUNGLASSES, self.identity, 0.0
+        )
+        self.assertGreaterEqual(quadrature.azimuth_points.min(), _SG_AZIMUTH_LO_DEG)
+        self.assertLessEqual(quadrature.azimuth_points.max(), _SG_AZIMUTH_HI_DEG)
+
+    def test_sg_azimuth_window_clamps_to_plus_minus_20_for_broad_distribution(self):
+        """A very hot plasma's 180° angular extent forces the azimuth window to clamp exactly to the `[-20°, +20°]` SG band, with the outermost GL nodes hugging both edges."""
+        sw = proton_params(temperature=1e10)
+        _, quadrature = get_angular_quadrature(
+            sw, self.response_grid, REGION_SUNGLASSES, self.identity, 0.0
+        )
+        # GL nodes lie strictly inside the band.
+        np.testing.assert_array_less(
+            quadrature.azimuth_points, _SG_AZIMUTH_HI_DEG + 1e-9
+        )
+        np.testing.assert_array_less(
+            _SG_AZIMUTH_LO_DEG - 1e-9, quadrature.azimuth_points
+        )
+        # The outermost 21-node GL node sits ≈0.125° inside the edge of a 40°
+        # window (analytically `20°·(1 - x_GL[-1])` ≈ 0.125°). 1.5° is a loose
+        # cushion above that — only meant to catch the case where the window
+        # collapses or shifts away from the edge entirely.
+        self.assertGreater(quadrature.azimuth_points.max(), _SG_AZIMUTH_HI_DEG - 1.5)
+        self.assertLess(quadrature.azimuth_points.min(), _SG_AZIMUTH_LO_DEG + 1.5)
+
+    def test_sg_elevation_window_clamps_into_passband_elevation_range(self):
+        """A hot plasma's saturated angular extent is clipped by the SG passband elevation range, so every elevation node lies inside that range."""
+        sw = proton_params(temperature=1e10)
+        _, quadrature = get_angular_quadrature(
+            sw, self.response_grid, REGION_SUNGLASSES, self.identity, 0.0
+        )
+        sg_el_lo, sg_el_hi = self.response_grid.sg_passband.elevation_range
+        self.assertGreaterEqual(quadrature.elevation_points.min(), sg_el_lo)
+        self.assertLessEqual(quadrature.elevation_points.max(), sg_el_hi)
+
+    def test_sg_quadrature_has_21_nodes_in_each_angular_dimension(self):
+        """The SG angular quadrature is built with the doc-prescribed (Nθ, Nφ) = (21, 21) Gauss-Legendre node counts."""
+        sw = proton_params()
+        _, quadrature = get_angular_quadrature(
+            sw, self.response_grid, REGION_SUNGLASSES, self.identity, 0.0
+        )
+        self.assertEqual(quadrature.elevation_points.shape, (21,))
+        self.assertEqual(quadrature.azimuth_points.shape, (21,))
+
+    def test_sin_cos_caches_match_their_angle_arrays(self):
+        """The precomputed sin/cos arrays on the quadrature exactly match `sin`/`cos` of the corresponding angle (in radians) at every node."""
+        sw = proton_params()
+        _, quadrature = get_angular_quadrature(
+            sw, self.response_grid, REGION_SUNGLASSES, self.identity, 0.0
+        )
+        np.testing.assert_allclose(
+            quadrature.sin_elevation, np.sin(np.radians(quadrature.elevation_points))
+        )
+        np.testing.assert_allclose(
+            quadrature.cos_elevation, np.cos(np.radians(quadrature.elevation_points))
+        )
+        np.testing.assert_allclose(
+            quadrature.sin_azimuth, np.sin(np.radians(quadrature.azimuth_points))
+        )
+        np.testing.assert_allclose(
+            quadrature.cos_azimuth, np.cos(np.radians(quadrature.azimuth_points))
+        )
+
+    def test_sg_region_skipped_when_bulk_far_from_sg_band(self):
+        """A bulk pointed at azimuth ≈ 90° is 70° away from the SG band and a narrow Maxwellian cannot bridge the gap, so the SG region is skipped."""
+        sw = proton_params(velocity_rtn=(-_BULK_SPEED_KM_S, 0.0, 0.0))
+        skip, quadrature = get_angular_quadrature(
+            sw, self.response_grid, REGION_SUNGLASSES, self.identity, 0.0
+        )
+        self.assertTrue(skip)
+        self.assertIsNone(quadrature)
+
+
+class TestGetAngularQuadratureOpenAperturePositive(unittest.TestCase):
+    """Tests for `get_angular_quadrature` on the OA+ region (az ∈ [+20°, +150°]) with a bulk pointed at az ≈ +90°."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.response = load_swapi_response(warm_cache_voltages=[_ESA_VOLTAGE])
+        cls.response_grid = cls.response.get_response_grid(_ESA_VOLTAGE, 1.0)
+        cls.identity = np.eye(3)
+        # Bulk pointed at az=+90°: identity rotation makes v_inst = (-v, 0, 0),
+        # giving azimuth = atan2(v, 0) = 90° and elevation = 0°.
+        cls.sw_bulk_at_90 = proton_params(
+            velocity_rtn=(-_BULK_SPEED_KM_S, 0.0, 0.0)
+        )
+
+    def test_oa_positive_region_is_active_for_bulk_at_90_deg_azimuth(self):
+        """A bulk at az ≈ +90° lies inside the OA+ band, so the region is active and a quadrature is returned."""
+        skip, quadrature = get_angular_quadrature(
+            self.sw_bulk_at_90,
+            self.response_grid,
+            REGION_OPEN_APERTURE_POS,
+            self.identity,
+            0.0,
+        )
+        self.assertFalse(skip)
+        self.assertIsNotNone(quadrature)
+
+    def test_oa_positive_azimuth_window_is_inside_20_to_150_degrees(self):
+        """For a bulk at az ≈ +90°, every OA+ azimuth node lies strictly inside the `[+20°, +150°]` OA+ band."""
+        _, quadrature = get_angular_quadrature(
+            self.sw_bulk_at_90,
+            self.response_grid,
+            REGION_OPEN_APERTURE_POS,
+            self.identity,
+            0.0,
+        )
+        self.assertGreaterEqual(
+            quadrature.azimuth_points.min(), _OA_AZIMUTH_INNER_DEG
+        )
+        self.assertLessEqual(
+            quadrature.azimuth_points.max(), _OA_AZIMUTH_OUTER_DEG
+        )
+
+    def test_oa_negative_region_is_skipped_for_bulk_at_plus_90_deg_azimuth(self):
+        """A bulk at az ≈ +90° is too far from the OA- band for a narrow Maxwellian to reach, so the OA- region is skipped."""
+        skip, quadrature = get_angular_quadrature(
+            self.sw_bulk_at_90,
+            self.response_grid,
+            REGION_OPEN_APERTURE_NEG,
+            self.identity,
+            0.0,
+        )
+        self.assertTrue(skip)
+        self.assertIsNone(quadrature)
+
+
+class TestGetAngularQuadratureOpenApertureNegative(unittest.TestCase):
+    """Tests for `get_angular_quadrature` on the OA- region (az ∈ [-150°, -20°]) with a bulk pointed at az ≈ -90°."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.response = load_swapi_response(warm_cache_voltages=[_ESA_VOLTAGE])
+        cls.response_grid = cls.response.get_response_grid(_ESA_VOLTAGE, 1.0)
+        cls.identity = np.eye(3)
+        # Bulk at az=-90°: v_inst = (+v, 0, 0) ⇒ az = atan2(-v, 0) = -90°.
+        cls.sw_bulk_at_minus_90 = proton_params(
+            velocity_rtn=(+_BULK_SPEED_KM_S, 0.0, 0.0)
+        )
+
+    def test_oa_negative_region_is_active_for_bulk_at_minus_90_deg_azimuth(self):
+        """A bulk at az ≈ -90° lies inside the OA- band, so the region is active and a quadrature is returned."""
+        skip, quadrature = get_angular_quadrature(
+            self.sw_bulk_at_minus_90,
+            self.response_grid,
+            REGION_OPEN_APERTURE_NEG,
+            self.identity,
+            0.0,
+        )
+        self.assertFalse(skip)
+        self.assertIsNotNone(quadrature)
+
+    def test_oa_negative_azimuth_window_is_inside_minus_150_to_minus_20(self):
+        """For a bulk at az ≈ -90°, every OA- azimuth node lies strictly inside the `[-150°, -20°]` OA- band."""
+        _, quadrature = get_angular_quadrature(
+            self.sw_bulk_at_minus_90,
+            self.response_grid,
+            REGION_OPEN_APERTURE_NEG,
+            self.identity,
+            0.0,
+        )
+        self.assertGreaterEqual(
+            quadrature.azimuth_points.min(), -_OA_AZIMUTH_OUTER_DEG
+        )
+        self.assertLessEqual(
+            quadrature.azimuth_points.max(), -_OA_AZIMUTH_INNER_DEG
+        )
+
+    def test_oa_positive_region_is_skipped_for_bulk_at_minus_90_deg_azimuth(self):
+        """A bulk at az ≈ -90° is too far from the OA+ band for a narrow Maxwellian to reach, so the OA+ region is skipped."""
+        skip, quadrature = get_angular_quadrature(
+            self.sw_bulk_at_minus_90,
+            self.response_grid,
+            REGION_OPEN_APERTURE_POS,
+            self.identity,
+            0.0,
+        )
+        self.assertTrue(skip)
+        self.assertIsNone(quadrature)
+
+
+class TestGetSpeedQuadrature(unittest.TestCase):
+    """Tests for `get_speed_quadrature` — the 15-node Gauss-Legendre quadrature over the intersection of `bulk_speed ± k·vth` and the per-elevation passband speed range."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.response = load_swapi_response(warm_cache_voltages=[_ESA_VOLTAGE])
+        cls.response_grid = cls.response.get_response_grid(_ESA_VOLTAGE, 1.0)
+
+    def test_speed_window_lies_inside_per_elevation_passband(self):
+        """For warm plasma the passband binds the window, so every GL node lies inside `[r_min(θ)·v0, r_max(θ)·v0]` at the given elevation."""
+        sw = proton_params()
+        _, speed_quadrature = get_speed_quadrature(
+            sw, self.response_grid, REGION_SUNGLASSES, 0.0
+        )
+        ratio_lo, ratio_hi = speed_ratio_range_at_elevation(
+            self.response_grid.sg_passband, 0.0
+        )
+        passband_lo = self.response_grid.central_speed * ratio_lo
+        passband_hi = self.response_grid.central_speed * ratio_hi
+        self.assertGreaterEqual(speed_quadrature.points.min(), passband_lo)
+        self.assertLessEqual(speed_quadrature.points.max(), passband_hi)
+
+    def test_cold_plasma_window_lies_inside_bulk_plus_minus_k_vth(self):
+        """For cold plasma (T = 480 K) at the passband center, the `bulk ± k·σ` interval is the binding constraint and every GL node sits inside it."""
+        sw = proton_params(
+            velocity_rtn=(0.0, -self.response_grid.central_speed, 0.0),
+            temperature=480.0,
+        )
+        sigma = thermal_speed(sw)
+        speed = bulk_speed(sw)
+        _, speed_quadrature = get_speed_quadrature(
+            sw, self.response_grid, REGION_SUNGLASSES, 0.0
+        )
+        window_lo = speed - SPEED_HALF_WIDTH_VTH * sigma
+        window_hi = speed + SPEED_HALF_WIDTH_VTH * sigma
+        self.assertGreaterEqual(speed_quadrature.points.min(), window_lo)
+        self.assertLessEqual(speed_quadrature.points.max(), window_hi)
+
+    def test_cold_plasma_outer_gl_nodes_sit_close_to_window_edges(self):
+        """For the same cold-plasma case, the outermost GL nodes sit within ~2 km/s of `bulk ± k·σ`, pinning the window endpoints to `bulk ± k·σ` rather than some narrower subset."""
+        sw = proton_params(
+            velocity_rtn=(0.0, -self.response_grid.central_speed, 0.0),
+            temperature=480.0,
+        )
+        sigma = thermal_speed(sw)
+        speed = bulk_speed(sw)
+        _, speed_quadrature = get_speed_quadrature(
+            sw, self.response_grid, REGION_SUNGLASSES, 0.0
+        )
+        window_lo = speed - SPEED_HALF_WIDTH_VTH * sigma
+        window_hi = speed + SPEED_HALF_WIDTH_VTH * sigma
+        self.assertLess(window_hi - speed_quadrature.points.max(), 2.0)
+        self.assertLess(speed_quadrature.points.min() - window_lo, 2.0)
+
+    def test_speed_quadrature_arrays_are_all_15_long(self):
+        """Each array on the returned `SpeedQuadrature` (points, weights, `speed_cubed_times_passband`) has the doc-prescribed Nv = 15 nodes."""
+        sw = proton_params()
+        _, speed_quadrature = get_speed_quadrature(
+            sw, self.response_grid, REGION_SUNGLASSES, 0.0
+        )
+        self.assertEqual(speed_quadrature.points.shape, (15,))
+        self.assertEqual(speed_quadrature.weights.shape, (15,))
+        self.assertEqual(speed_quadrature.speed_cubed_times_passband.shape, (15,))
+
+    def test_returns_skip_when_bulk_speed_window_does_not_overlap_passband(self):
+        """A bulk so far above the passband that `bulk - k·σ` exceeds the upper edge yields skip=True and a `None` quadrature."""
+        sw = proton_params(
+            velocity_rtn=(0.0, -1500.0, 0.0), temperature=10_000.0
+        )
+        skip, speed_quadrature = get_speed_quadrature(
+            sw, self.response_grid, REGION_SUNGLASSES, 0.0
+        )
+        self.assertTrue(skip)
+        self.assertIsNone(speed_quadrature)
+
+    def test_uses_region_specific_passband(self):
+        """With a hot plasma so the passband binds the window in both regions, the SG and OA quadratures land on their own region's passband edge and the two windows are distinct."""
+        sw = proton_params(temperature=1_000_000.0)
+        _, sg_quad = get_speed_quadrature(
+            sw, self.response_grid, REGION_SUNGLASSES, 0.0
+        )
+        _, oa_quad = get_speed_quadrature(
+            sw, self.response_grid, REGION_OPEN_APERTURE_POS, 0.0
+        )
+        sg_ratio_lo, _ = speed_ratio_range_at_elevation(
+            self.response_grid.sg_passband, 0.0
+        )
+        oa_ratio_lo, _ = speed_ratio_range_at_elevation(
+            self.response_grid.oa_passband, 0.0
+        )
+        sg_lo = self.response_grid.central_speed * sg_ratio_lo
+        oa_lo = self.response_grid.central_speed * oa_ratio_lo
+        # Each quadrature's lower edge sits at its own region's passband edge.
+        self.assertGreaterEqual(sg_quad.points.min(), sg_lo)
+        self.assertGreaterEqual(oa_quad.points.min(), oa_lo)
+        # And the two windows are actually distinct — the SG and OA passbands
+        # have different speed-ratio bounds, so a region mix-up would be
+        # silently caught here.
+        self.assertNotEqual(sg_quad.points.min(), oa_quad.points.min())
+
+    def test_speed_cubed_times_passband_is_v3_times_normalized_passband_at_each_node(self):
+        """The `speed_cubed_times_passband` array equals `v³ · P(θ, v/v0) / P(0, 1)` reconstructed from `interpolate_passband` at every GL node."""
+        sw = proton_params()
+        _, speed_quadrature = get_speed_quadrature(
+            sw, self.response_grid, REGION_SUNGLASSES, 0.0
+        )
+        on_axis_peak = interpolate_passband(
+            self.response_grid.sg_passband, 0.0, 1.0
+        )
+        expected = np.array(
+            [
+                v**3
+                * interpolate_passband(
+                    self.response_grid.sg_passband,
+                    0.0,
+                    v / self.response_grid.central_speed,
+                )
+                / on_axis_peak
+                for v in speed_quadrature.points
+            ]
+        )
+        np.testing.assert_allclose(
+            speed_quadrature.speed_cubed_times_passband, expected
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/science/solar_wind/test_open_aperture_trimming.py b/tests/swapi/l3a/science/solar_wind/test_open_aperture_trimming.py
new file mode 100644
index 000000000..620bdbd3a
--- /dev/null
+++ b/tests/swapi/l3a/science/solar_wind/test_open_aperture_trimming.py
@@ -0,0 +1,274 @@
+import math
+import unittest
+
+import numpy as np
+
+from imap_l3_processing.swapi.l3a.science.solar_wind.open_aperture_trimming import (
+    OA_SCAN_RESOLUTION,
+    trim_oa_azimuth_by_integrand,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    SolarWindParams,
+)
+from imap_l3_processing.swapi.response.azimuthal_transmission import (
+    AzimuthalTransmissionGrid,
+)
+from imap_l3_processing.swapi.response.passband_grid import build_passband_grid
+from imap_l3_processing.swapi.response.swapi_response import ResponseGrid
+from tests.swapi._helpers import NOMINAL_SWAPI_TO_RTN_ROTATION, proton_params
+from tests.swapi.response.test_passband_grid import _gaussian_values_df
+
+
+# --- fixtures ---------------------------------------------------------------
+
+
+def _make_response_grid(
+    central_speed: float = 450.0,
+    azimuthal_transmission: np.ndarray | None = None,
+    azimuthal_transmission_spacing: float = 1.0,
+) -> ResponseGrid:
+    if azimuthal_transmission is None:
+        # T(|az|) = 1 sampled on [0°, 180°] at 1° spacing → 181 nodes.
+        azimuthal_transmission = np.ones(181)
+    df = _gaussian_values_df(sigma_el=2.0)
+    sg_passband = build_passband_grid(df)
+    oa_passband = build_passband_grid(df)
+    return ResponseGrid(
+        sg_passband=sg_passband,
+        oa_passband=oa_passband,
+        central_speed=central_speed,
+        # Picked arbitrarily — only the count-rate prefactor scales with it,
+        # and tests here either compare to literal sentinels or assert
+        # window-shape properties that are invariant to overall amplitude.
+        central_effective_area=0.5,
+        azimuthal_transmission=AzimuthalTransmissionGrid(
+            values=azimuthal_transmission,
+            spacing=azimuthal_transmission_spacing,
+        ),
+    )
+
+
+def _proton_params_at_elevation(
+    speed: float, bulk_el_deg: float, density: float = 5.0, temperature: float = 1.0e5
+) -> SolarWindParams:
+    v_x_swapi = -speed * math.cos(math.radians(bulk_el_deg))
+    v_z_swapi = -speed * math.sin(math.radians(bulk_el_deg))
+    v_rtn = NOMINAL_SWAPI_TO_RTN_ROTATION @ np.array(
+        [v_x_swapi, 0.0, v_z_swapi]
+    )
+    return proton_params(
+        velocity_rtn=tuple(v_rtn), density=density, temperature=temperature
+    )
+
+
+def _trim(
+    rg: ResponseGrid,
+    sw: SolarWindParams,
+    *,
+    azimuth_lo: float,
+    azimuth_hi: float,
+    sg_rate: float,
+    rotation_matrix: np.ndarray | None = None,
+    min_elevation: float = -10.0,
+    max_elevation: float = 10.0,
+):
+    if rotation_matrix is None:
+        rotation_matrix = NOMINAL_SWAPI_TO_RTN_ROTATION
+  
+    return trim_oa_azimuth_by_integrand(
+        rg,
+        sw,
+        rotation_matrix,
+        min_elevation,
+        max_elevation,
+        azimuth_lo=azimuth_lo,
+        azimuth_hi=azimuth_hi,
+        sg_rate=sg_rate,
+    )
+
+
+# --- tests ------------------------------------------------------------------
+
+
+class TestSkipsWhenAzimuthWindowIsEmpty(unittest.TestCase):
+    """Tests for `trim_oa_azimuth_by_integrand`, degenerate input-window branch."""
+
+    def test_skips_when_window_has_zero_width(self):
+        """A zero-width azimuth clamp short-circuits to the (0, 0) sentinel without scanning."""
+        rg = _make_response_grid()
+        sw = proton_params()
+        lo, hi = _trim(rg, sw, azimuth_lo=30.0, azimuth_hi=30.0, sg_rate=10.0)
+        self.assertEqual((lo, hi), (0.0, 0.0))
+
+    def test_skips_when_window_is_inverted(self):
+        """An inverted clamp (`hi < lo`) also short-circuits to the (0, 0) sentinel."""
+        rg = _make_response_grid()
+        sw = proton_params()
+        lo, hi = _trim(rg, sw, azimuth_lo=50.0, azimuth_hi=20.0, sg_rate=10.0)
+        self.assertEqual((lo, hi), (0.0, 0.0))
+
+
+class TestSkipsWhenSgRateRelativeFloorDominates(unittest.TestCase):
+    """Tests for `trim_oa_azimuth_by_integrand`, `1e-3·sg_rate` skip branch."""
+
+    def test_skips_when_oa_upper_bound_below_sg_relative_floor(self):
+        """A cold bulk far from OA with a huge sg_rate trips the relative-fraction floor and skips OA."""
+        rg = _make_response_grid()
+        sw = proton_params(temperature=1.0e4)
+        lo, hi = _trim(rg, sw, azimuth_lo=20.0, azimuth_hi=150.0, sg_rate=1.0e6)
+        self.assertEqual((lo, hi), (0.0, 0.0))
+
+
+class TestSkipsWhenAbsoluteRateFloorDominates(unittest.TestCase):
+    """Tests for `trim_oa_azimuth_by_integrand`, 0.1 Hz absolute-floor skip branch."""
+
+    def test_skips_when_oa_upper_bound_below_absolute_floor(self):
+        """With sg_rate=0, the 0.1 Hz absolute floor alone forces a skip for a cold bulk far from OA."""
+        rg = _make_response_grid()
+        sw = proton_params(temperature=1.0e4)
+        lo, hi = _trim(rg, sw, azimuth_lo=20.0, azimuth_hi=150.0, sg_rate=0.0)
+        self.assertEqual((lo, hi), (0.0, 0.0))
+
+    def test_skips_when_sg_relative_below_absolute_floor(self):
+        """When `1e-3·sg_rate = 0.05 Hz < 0.1 Hz`, the `max(...)` picks the absolute floor and still skips."""
+        rg = _make_response_grid()
+        sw = proton_params(temperature=1.0e4)
+        lo, hi = _trim(rg, sw, azimuth_lo=20.0, azimuth_hi=150.0, sg_rate=50.0)
+        self.assertEqual((lo, hi), (0.0, 0.0))
+
+
+class TestReturnsTrimmedWindowWhenOaIsRelevant(unittest.TestCase):
+    """Tests for `trim_oa_azimuth_by_integrand`, well-aligned bulk shared-fixture branch."""
+
+    def setUp(self):
+        # Under NOMINAL_SWAPI_TO_RTN_ROTATION, bulk along -T_RTN (i.e.
+        # (0, -450, 0)) maps to v_swapi = (-450, 0, 0) → azimuth_inst = +90°.
+        self.rg = _make_response_grid()
+        self.sw = proton_params(velocity_rtn=(0.0, -450.0, 0.0))
+        self.az_lo_in = 20.0
+        self.az_hi_in = 150.0
+        # zero sg_rate keeps only the 0.1 Hz floor — the upper-bound estimate
+        # for a 5 cm⁻³, 100 kK plasma well aligned with OA+ exceeds that easily.
+        self.sg_rate = 0.0
+        self.lo, self.hi = _trim(
+            self.rg,
+            self.sw,
+            azimuth_lo=self.az_lo_in,
+            azimuth_hi=self.az_hi_in,
+            sg_rate=self.sg_rate,
+        )
+
+    def test_returned_window_has_positive_width(self):
+        """A bulk deep inside OA+ produces a non-empty trimmed window."""
+        self.assertGreater(self.hi, self.lo)
+
+    def test_returned_window_lies_inside_input_clamp(self):
+        """The trimmed window is contained in the input azimuth clamp."""
+        self.assertGreaterEqual(self.lo, self.az_lo_in)
+        self.assertLessEqual(self.hi, self.az_hi_in)
+
+    def test_returned_endpoints_lie_on_the_scan_grid(self):
+        """Both endpoints coincide with nodes of `linspace(az_lo, az_hi, OA_SCAN_RESOLUTION)`."""
+        scan = np.linspace(self.az_lo_in, self.az_hi_in, OA_SCAN_RESOLUTION)
+        self.assertTrue(np.any(np.isclose(scan, self.lo)))
+        self.assertTrue(np.any(np.isclose(scan, self.hi)))
+
+    def test_returned_window_brackets_the_bulk_azimuth(self):
+        """The trimmed window strictly contains the bulk azimuth (+90° here)."""
+        bulk_azimuth = 90.0
+        self.assertLess(self.lo, bulk_azimuth)
+        self.assertGreater(self.hi, bulk_azimuth)
+
+
+class TestTrimAt1eMinus6Threshold(unittest.TestCase):
+    """Tests for `trim_oa_azimuth_by_integrand`, OA_SCAN_THRESHOLD endpoint selection."""
+
+    def test_endpoints_expand_one_node_past_threshold_crossings(self):
+        """A step-function transmission plateau drives the trim to return one scan node outside the first/last above-threshold node."""
+        # Hand-built scenario with a transmission curve that's zero outside a
+        # narrow azimuth band, so the scan integrand is a step function we can
+        # reason about exactly without re-running production code.
+        #
+        # T(|az|) is 1 inside [85°, 95°] and 0 elsewhere. With a uniform-Maxwellian
+        # bulk (scaling `M(φ)` by zero kills that whole node regardless), the
+        # scan g(φ) on the 64-node grid linspace(20, 150, 64) is non-zero at
+        # exactly the scan nodes whose nearest 1° transmission sample is in the
+        # plateau. linspace(20, 150, 64) has spacing 130/63 ≈ 2.0635°, and the
+        # nodes inside [85°, 95°] are indices 32, 33, 34 (≈86.03°, 88.10°,
+        # 90.16°, 92.22°, 94.29° — but azimuth lookup quantizes to integer °, so
+        # the plateau is exactly nodes whose floor(|az|) ∈ [85, 95)).
+        transmission = np.zeros(181)
+        transmission[85:95] = 1.0  # T = 1 on [85°, 94°]
+        rg = _make_response_grid(azimuthal_transmission=transmission)
+        sw = proton_params(velocity_rtn=(0.0, -450.0, 0.0))  # bulk_az = +90°
+
+        lo, hi = _trim(rg, sw, azimuth_lo=20.0, azimuth_hi=150.0, sg_rate=0.0)
+
+        scan = np.linspace(20.0, 150.0, OA_SCAN_RESOLUTION)
+        # Find which nodes lie in [85°, 94°] — those are the "above-threshold"
+        # nodes for this transmission. The trim must return one node outside
+        # the first/last of those indices.
+        in_plateau = (scan >= 85.0) & (scan < 95.0)
+        plateau_idx = np.where(in_plateau)[0]
+        self.assertGreater(plateau_idx.size, 0)
+        expected_lo = scan[max(int(plateau_idx[0]) - 1, 0)]
+        expected_hi = scan[min(int(plateau_idx[-1]) + 1, OA_SCAN_RESOLUTION - 1)]
+        self.assertAlmostEqual(lo, expected_lo)
+        self.assertAlmostEqual(hi, expected_hi)
+
+
+class TestSymmetryBetweenOaPositiveAndNegative(unittest.TestCase):
+    """Tests for `trim_oa_azimuth_by_integrand`, OA+/OA- mirror-symmetry contract."""
+
+    def test_mirror_symmetric_bulk_yields_mirror_symmetric_windows(self):
+        """Bulks at ±90° produce OA+ and OA- windows that are exact mirror images of each other."""
+        rg = _make_response_grid()
+
+        # Under NOMINAL_SWAPI_TO_RTN_ROTATION:
+        #   v_rtn = (0, -450, 0) → v_swapi = (-450, 0, 0) → az_inst = +90° (OA+).
+        #   v_rtn = (0, +450, 0) → v_swapi = (+450, 0, 0) → az_inst = -90° (OA-).
+        sw_pos = proton_params(velocity_rtn=(0.0, -450.0, 0.0))
+        sw_neg = proton_params(velocity_rtn=(0.0, +450.0, 0.0))
+
+        lo_pos, hi_pos = _trim(rg, sw_pos, azimuth_lo=20.0, azimuth_hi=150.0, sg_rate=0.0)
+        # OA-: same magnitudes, mirrored around 0.
+        lo_neg, hi_neg = _trim(rg, sw_neg, azimuth_lo=-150.0, azimuth_hi=-20.0, sg_rate=0.0)
+
+        # The implementation picks scan-grid nodes by index, and
+        # `linspace(20, 150, N)` mirrors `linspace(-150, -20, N)` up to a
+        # single ULP from `linspace`'s internal accumulation order.
+        np.testing.assert_allclose(lo_neg, -hi_pos, atol=1e-12, rtol=0)
+        np.testing.assert_allclose(hi_neg, -lo_pos, atol=1e-12, rtol=0)
+
+
+class TestSkipPolicyAgainstSgRate(unittest.TestCase):
+    """Tests for `trim_oa_azimuth_by_integrand`, sg_rate-driven skip transition."""
+
+    def test_high_sg_rate_forces_skip_for_otherwise_aligned_bulk(self):
+        """A bulk that yields a real window at sg_rate=0 flips to the (0, 0) sentinel once sg_rate is cranked above the OA upper bound."""
+        rg = _make_response_grid()
+        sw = proton_params(velocity_rtn=(0.0, -450.0, 0.0))  # az_inst = +90°
+
+        lo_open, hi_open = _trim(
+            rg, sw, azimuth_lo=20.0, azimuth_hi=150.0, sg_rate=0.0
+        )
+        self.assertGreater(hi_open, lo_open)
+
+        lo, hi = _trim(rg, sw, azimuth_lo=20.0, azimuth_hi=150.0, sg_rate=1.0e15)
+        self.assertEqual((lo, hi), (0.0, 0.0))
+
+
+class TestScanAtBulkElevationClampedToOaRange(unittest.TestCase):
+    """Tests for `trim_oa_azimuth_by_integrand`, bulk-elevation clamp to `[min_elevation, max_elevation]`."""
+
+    def test_bulk_elevation_above_range_yields_non_empty_window(self):
+        """A bulk at +20° elevation (above the +10° OA cap) still yields a non-empty trimmed window after the scan elevation is clamped to +10°."""
+        rg = _make_response_grid()
+        sw = _proton_params_at_elevation(speed=450.0, bulk_el_deg=20.0)
+
+        lo, hi = _trim(rg, sw, azimuth_lo=20.0, azimuth_hi=150.0, sg_rate=0.0)
+        self.assertGreater(hi, lo)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/science/solar_wind/test_optimizer.py b/tests/swapi/l3a/science/solar_wind/test_optimizer.py
new file mode 100644
index 000000000..92d1c6bdf
--- /dev/null
+++ b/tests/swapi/l3a/science/solar_wind/test_optimizer.py
@@ -0,0 +1,199 @@
+import unittest
+from unittest.mock import patch
+
+import numpy as np
+import scipy.optimize
+
+from imap_l3_processing.constants import (
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    build_solar_wind_fit_context,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.optimizer import (
+    OptimizeSolarWindParamsResult,
+    optimize_solar_wind_params,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    N_STATE,
+    SolarWindParams,
+)
+from tests.swapi._helpers import (
+    NOMINAL_SWAPI_TO_RTN_ROTATION,
+    REALISTIC_ESA_VOLTAGES,
+    load_swapi_response,
+    proton_params,
+    synthesize_count_rates,
+)
+
+
+def _build_proton_fit_context(count_rate: np.ndarray):
+    response = load_swapi_response(warm_cache_voltages=REALISTIC_ESA_VOLTAGES)
+    n_sweeps = len(REALISTIC_ESA_VOLTAGES)
+    rotation_matrices = np.broadcast_to(
+        NOMINAL_SWAPI_TO_RTN_ROTATION, (n_sweeps, 3, 3)
+    ).copy()
+    return build_solar_wind_fit_context(
+        count_rate=count_rate,
+        esa_voltage=REALISTIC_ESA_VOLTAGES,
+        swapi_response=response,
+        central_effective_area_scale=1.0,
+        rotation_matrices=rotation_matrices,
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    )
+
+
+def _synthetic_count_rate_for(sw_params: SolarWindParams) -> np.ndarray:
+    """Forward-model deadtime-applied count rates from `sw_params` against
+    the fixture voltages. Used to seed both the "recovers known params" and
+    cache-counting tests with a realistic measurement vector.
+
+    Two-step: build a placeholder context with zero counts, run the forward
+    model to get ideal rates, then apply the deadtime factor exactly the way
+    `_Evaluator._eval` does (so the residual at the truth is zero)."""
+    ctx = _build_proton_fit_context(np.zeros_like(REALISTIC_ESA_VOLTAGES))
+    return synthesize_count_rates(ctx, sw_params)
+
+
+class TestOptimizeSolarWindParamsResultMSE(unittest.TestCase):
+    """Tests for `OptimizeSolarWindParamsResult.mse`."""
+
+    def test_mse_is_mean_of_squared_residuals(self):
+        """Given a residuals vector [1, -2, 3], mse returns 14/3 — the per-bin mean of squared residuals."""
+        result = OptimizeSolarWindParamsResult(
+            sw_params=proton_params(),
+            residuals=np.array([1.0, -2.0, 3.0]),
+            jacobian=np.zeros((3, N_STATE)),
+            success=True,
+        )
+        self.assertAlmostEqual(result.mse, 14.0 / 3.0)
+
+    def test_mse_handles_all_zero_residuals_without_dividing_by_zero(self):
+        """At the truth (noise-free fit) all residuals are zero and mse reports exactly 0.0 rather than NaN/Inf, so the wrong-basin chi^2 comparator can be applied unconditionally."""
+        result = OptimizeSolarWindParamsResult(
+            sw_params=proton_params(),
+            residuals=np.zeros(5),
+            jacobian=np.zeros((5, N_STATE)),
+            success=True,
+        )
+        self.assertEqual(result.mse, 0.0)
+
+
+class TestOptimizeSolarWindParamsRecoversTruth(unittest.TestCase):
+    """End-to-end tests for `optimize_solar_wind_params` driving an LM fit to a known synthetic solar wind state."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.true_params = proton_params()
+        cls.count_rate = _synthetic_count_rate_for(cls.true_params)
+        cls.ctx = _build_proton_fit_context(count_rate=cls.count_rate)
+        # Initial guess perturbed in density (+10%), speed (+3%), and
+        # temperature (+20%). Sized so LM converges in one basin without
+        # invoking the wrong-basin flip; the basin-of-attraction bounds live
+        # in docs/swapi/solar-wind-moments.md § Wrong-basin detection.
+        cls.initial_guess = proton_params(
+            density=cls.true_params.density * 1.1,
+            velocity_rtn=cls.true_params.bulk_velocity_rtn * 1.03,
+            temperature=cls.true_params.temperature * 1.2,
+        )
+        cls.result = optimize_solar_wind_params(cls.initial_guess, cls.ctx)
+
+    def test_optimizer_recovers_density(self):
+        """Starting from a +10% density perturbation against noise-free synthetic data, the fitted density returns to the truth within 0.1%."""
+        np.testing.assert_allclose(
+            self.result.sw_params.density,
+            self.true_params.density,
+            rtol=1e-3,
+        )
+
+    def test_optimizer_recovers_temperature(self):
+        """Starting from a +20% temperature perturbation against noise-free synthetic data, the fitted temperature returns to the truth within 0.1%."""
+        np.testing.assert_allclose(
+            self.result.sw_params.temperature,
+            self.true_params.temperature,
+            rtol=1e-3,
+        )
+
+    def test_optimizer_recovers_bulk_velocity(self):
+        """Starting from a +3% speed perturbation against noise-free synthetic data, the fitted bulk velocity returns to the truth within 0.5 km/s on each RTN component."""
+        np.testing.assert_allclose(
+            self.result.sw_params.bulk_velocity_rtn,
+            self.true_params.bulk_velocity_rtn,
+            atol=0.5,
+        )
+
+    def test_optimizer_reports_success(self):
+        """LM converges cleanly on the well-posed synthetic problem and the result's success flag is True."""
+        self.assertTrue(self.result.success)
+
+    def test_residuals_at_solution_are_small(self):
+        """At the converged solution against noise-free data, mse is below (peak_rate)^2 * 1e-4 — residuals are limited only by LM's xtol=1e-4 tolerance."""
+        peak_rate_squared = float(np.max(self.count_rate)) ** 2
+        self.assertLess(self.result.mse, peak_rate_squared * 1e-4)
+
+
+class TestOptimizeSolarWindParamsResultShape(unittest.TestCase):
+    """Tests for `optimize_solar_wind_params` result-object shape and type contracts the wrong-basin detector and uncertainty derivation depend on."""
+
+    @classmethod
+    def setUpClass(cls):
+        true_params = proton_params()
+        count_rate = _synthetic_count_rate_for(true_params)
+        cls.ctx = _build_proton_fit_context(count_rate=count_rate)
+        cls.result = optimize_solar_wind_params(true_params, cls.ctx)
+
+    def test_sw_params_is_a_solar_wind_params(self):
+        """The returned `sw_params` field is a `SolarWindParams` instance, not a raw state vector."""
+        self.assertIsInstance(self.result.sw_params, SolarWindParams)
+
+    def test_residuals_length_matches_ctx_count_rate(self):
+        """The residuals array has one entry per observed count-rate bin in the fit context."""
+        self.assertEqual(self.result.residuals.shape, self.ctx.count_rate.shape)
+
+    def test_jacobian_shape_is_n_residuals_by_n_state(self):
+        """The jacobian has shape (n_residuals, N_STATE) — one row per residual, one column per fit parameter."""
+        self.assertEqual(
+            self.result.jacobian.shape, (self.ctx.count_rate.size, N_STATE)
+        )
+
+    def test_success_is_bool(self):
+        """The success field is a plain Python bool rather than numpy's bool-like wrapper."""
+        self.assertIsInstance(self.result.success, bool)
+
+    def test_sw_params_carries_context_mass(self):
+        """The fitted `SolarWindParams.mass` is propagated from `ctx.mass_kg` rather than defaulting to proton mass."""
+        self.assertEqual(self.result.sw_params.mass, self.ctx.mass_kg)
+
+
+class TestOptimizerLeastSquaresKwargs(unittest.TestCase):
+    """Tests that `optimize_solar_wind_params` invokes scipy with the doc-pinned LM kwargs."""
+
+    def test_uses_lm_with_xtol_from_doc_spec(self):
+        """When the optimizer runs, scipy.optimize.least_squares is called with `method='lm'` and `xtol=1e-4` per the doc spec."""
+        true_params = proton_params()
+        count_rate = _synthetic_count_rate_for(true_params)
+        ctx = _build_proton_fit_context(count_rate=count_rate)
+
+        with patch(
+            "imap_l3_processing.swapi.l3a.science.solar_wind.optimizer.scipy.optimize.least_squares"
+        ) as mock_least_squares:
+            mock_least_squares.return_value = scipy.optimize.OptimizeResult(
+                x=true_params.to_vector(),
+                fun=np.zeros_like(count_rate),
+                jac=np.zeros((count_rate.size, N_STATE)),
+                success=True,
+            )
+
+            optimize_solar_wind_params(true_params, ctx)
+
+            kwargs = mock_least_squares.call_args.kwargs
+            self.assertEqual(kwargs["method"], "lm")
+            self.assertEqual(kwargs["xtol"], 1e-4)
+
+
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/science/solar_wind/test_params.py b/tests/swapi/l3a/science/solar_wind/test_params.py
new file mode 100644
index 000000000..4e4fa6ba7
--- /dev/null
+++ b/tests/swapi/l3a/science/solar_wind/test_params.py
@@ -0,0 +1,161 @@
+import math
+import unittest
+
+import numpy as np
+
+from imap_l3_processing.constants import (
+    BOLTZMANN_CONSTANT_JOULES_PER_KELVIN,
+    METERS_PER_KILOMETER,
+    PROTON_MASS_KG,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    LOG_DENSITY_IDX,
+    LOG_TEMPERATURE_IDX,
+    N_STATE,
+    VELOCITY_SLICE,
+    SolarWindParams,
+    bulk_angles_in_instrument_frame,
+    bulk_speed,
+    temperature_to_thermal_speed,
+    thermal_speed,
+    thermal_speed_to_temperature,
+)
+from tests.swapi._helpers import proton_params
+
+
+class TestSolarWindParamsToVector(unittest.TestCase):
+    """Tests for `SolarWindParams.to_vector`."""
+
+    def test_density_and_temperature_are_log_encoded(self):
+        """A params object with density 5 and temperature 1e5 produces a state whose density and temperature slots equal log(5) and log(1e5)."""
+        sw = proton_params(density=5.0, temperature=100_000.0)
+        state = sw.to_vector()
+        self.assertAlmostEqual(state[LOG_DENSITY_IDX], math.log(5.0))
+        self.assertAlmostEqual(state[LOG_TEMPERATURE_IDX], math.log(100_000.0))
+
+    def test_velocity_is_stored_unchanged(self):
+        """The three velocity components are written into the velocity slice verbatim, without any rotation or rescaling."""
+        velocity = np.array([-450.0, 10.0, -5.0])
+        sw = proton_params(velocity_rtn=tuple(velocity))
+        state = sw.to_vector()
+        np.testing.assert_array_equal(state[VELOCITY_SLICE], velocity)
+
+    def test_vector_has_n_state_length(self):
+        """The encoded vector has exactly `N_STATE` elements so it matches the optimizer's expected length."""
+        self.assertEqual(proton_params().to_vector().shape, (N_STATE,))
+
+
+class TestSolarWindParamsFromVector(unittest.TestCase):
+    """Tests for `SolarWindParams.from_vector`, the inverse of `to_vector`."""
+
+    def test_from_vector_inverts_to_vector(self):
+        """Encoding a params object to a vector and decoding it back recovers the original density, temperature, velocity, and mass."""
+        original = proton_params(
+            density=3.7, velocity_rtn=(-420.0, 5.0, -2.0), temperature=85_000.0
+        )
+        round_tripped = SolarWindParams.from_vector(
+            original.to_vector(), mass=original.mass
+        )
+        self.assertAlmostEqual(round_tripped.density, original.density)
+        self.assertAlmostEqual(round_tripped.temperature, original.temperature)
+        np.testing.assert_array_equal(
+            round_tripped.bulk_velocity_rtn, original.bulk_velocity_rtn
+        )
+        self.assertEqual(round_tripped.mass, original.mass)
+
+    def test_from_vector_carries_mass_through_unchanged(self):
+        """Mass is not encoded in the state vector, so the caller-supplied species mass appears on the decoded params object unchanged."""
+        state = np.array([0.0, 0.0, -400.0, 0.0, 0.0])
+        sw_proton = SolarWindParams.from_vector(state, mass=PROTON_MASS_KG)
+        self.assertEqual(sw_proton.mass, PROTON_MASS_KG)
+
+
+class TestBulkSpeed(unittest.TestCase):
+    """Tests for `bulk_speed`."""
+
+    def test_returns_magnitude_of_bulk_velocity(self):
+        """A bulk velocity of (-300, 40, -50) km/s returns the Euclidean norm sqrt(300^2 + 40^2 + 50^2)."""
+        sw = proton_params(velocity_rtn=(-300.0, 40.0, -50.0))
+        expected = math.sqrt(300.0**2 + 40.0**2 + 50.0**2)
+        self.assertAlmostEqual(bulk_speed(sw), expected)
+
+    def test_zero_velocity_returns_zero(self):
+        """A bulk velocity of (0, 0, 0) returns 0 exactly, with no divide-by-zero artefacts."""
+        sw = proton_params(velocity_rtn=(0.0, 0.0, 0.0))
+        self.assertEqual(bulk_speed(sw), 0.0)
+
+
+class TestBulkAnglesInInstrumentFrame(unittest.TestCase):
+    """Tests for `bulk_angles_in_instrument_frame`, which returns (azimuth, elevation) in degrees in the instrument XYZ frame:
+    azimuth = atan2(-v_x_inst, -v_y_inst), elevation = asin(-v_z_inst / |v|)."""
+
+    def test_velocity_along_minus_y_inst_returns_zero_angles(self):
+        """Under identity rotation, an RTN wind along -Y becomes v_inst = (0, -450, 0) and yields azimuth = 0 and elevation = 0."""
+        sw = proton_params(velocity_rtn=(0.0, -450.0, 0.0))
+        azimuth, elevation = bulk_angles_in_instrument_frame(sw, np.eye(3))
+        self.assertAlmostEqual(azimuth, 0.0)
+        self.assertAlmostEqual(elevation, 0.0)
+
+    def test_velocity_with_negative_x_inst_component_yields_positive_azimuth(self):
+        """A negative instrument-X component (v_inst_x = -50) gives a strictly positive azimuth, since azimuth = atan2(+50, +450) > 0."""
+        sw = proton_params(velocity_rtn=(-50.0, -450.0, 0.0))
+        azimuth, _ = bulk_angles_in_instrument_frame(sw, np.eye(3))
+        self.assertGreater(azimuth, 0.0)
+
+    def test_velocity_with_positive_z_inst_component_yields_negative_elevation(self):
+        """A positive instrument-Z component (v_inst_z = +50) gives a strictly negative elevation, since elevation = asin(-50/|v|) < 0."""
+        sw = proton_params(velocity_rtn=(0.0, -450.0, 50.0))
+        _, elevation = bulk_angles_in_instrument_frame(sw, np.eye(3))
+        self.assertLess(elevation, 0.0)
+
+    def test_uses_rotation_argument_to_transform_into_instrument_frame(self):
+        """When the rotation maps +Y_inst to -X_RTN, an RTN wind along -X_RTN lands along +Y_inst and produces azimuth = atan2(0, -1) = +/-180 degrees, catching regressions that drop the rotation."""
+        rotation_xyz_to_rtn = np.array(
+            [[0.0, -1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 1.0]]
+        )
+        sw = proton_params(velocity_rtn=(-450.0, 0.0, 0.0))
+        azimuth, _ = bulk_angles_in_instrument_frame(sw, rotation_xyz_to_rtn)
+        self.assertAlmostEqual(abs(azimuth), 180.0, places=10)
+
+
+class TestThermalSpeedAndTemperature(unittest.TestCase):
+    """Tests for `thermal_speed`, `temperature_to_thermal_speed`, and `thermal_speed_to_temperature`
+    - the Maxwellian sigma = sqrt(kT/m) conversions in km/s."""
+
+    def test_thermal_speed_matches_analytic_formula(self):
+        """For a proton at 1e5 K, `thermal_speed` returns the closed-form sqrt(k*T/m) value in km/s."""
+        sw = proton_params(temperature=100_000.0)
+        expected_km_s = (
+            math.sqrt(BOLTZMANN_CONSTANT_JOULES_PER_KELVIN * 100_000.0 / PROTON_MASS_KG)
+            / METERS_PER_KILOMETER
+        )
+        self.assertAlmostEqual(thermal_speed(sw), expected_km_s)
+
+    def test_temperature_to_thermal_speed_round_trips_via_inverse(self):
+        """For temperatures spanning 1e3 to 1e7 K, converting to thermal speed and back
+        via the inverse recovers the original temperature."""
+        for temperature_k in [1_000.0, 1e5, 1e7]:
+            with self.subTest(temperature=temperature_k):
+                speed = temperature_to_thermal_speed(PROTON_MASS_KG, temperature_k)
+                recovered = thermal_speed_to_temperature(speed, PROTON_MASS_KG)
+                self.assertAlmostEqual(recovered, temperature_k)
+
+    def test_thermal_speed_scales_with_sqrt_of_temperature(self):
+        """At fixed mass, quadrupling the temperature (50 kK to 200 kK) doubles the thermal speed,
+        confirming the sigma proportional to sqrt(T) scaling."""
+        speed_low = temperature_to_thermal_speed(PROTON_MASS_KG, 50_000.0)
+        speed_high = temperature_to_thermal_speed(PROTON_MASS_KG, 200_000.0)
+        np.testing.assert_allclose(speed_high / speed_low, 2.0)
+
+    def test_thermal_speed_uses_params_temperature_and_mass(self):
+        """`thermal_speed(SolarWindParams)` is a wrapper that reads T and m from the params and matches
+        a direct `temperature_to_thermal_speed` call with the same inputs."""
+        sw = proton_params(temperature=120_000.0)
+        np.testing.assert_allclose(
+            thermal_speed(sw),
+            temperature_to_thermal_speed(PROTON_MASS_KG, 120_000.0),
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/science/solar_wind/test_uncertainties.py b/tests/swapi/l3a/science/solar_wind/test_uncertainties.py
new file mode 100644
index 000000000..0821a6deb
--- /dev/null
+++ b/tests/swapi/l3a/science/solar_wind/test_uncertainties.py
@@ -0,0 +1,289 @@
+import unittest
+from unittest.mock import MagicMock
+
+import numpy as np
+from uncertainties import covariance_matrix
+
+from imap_l3_processing.constants import PROTON_MASS_KG
+from imap_l3_processing.swapi.l3a.science.solar_wind.uncertainties import (
+    compute_hc3_parameter_covariance,
+    derive_uncertainties,
+    make_correlated_velocity,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    LOG_DENSITY_IDX,
+    N_STATE,
+    SolarWindParams,
+)
+
+
+# Module-level fixtures: a typical proton state vector.
+_TYPICAL_DENSITY = 5.0
+_TYPICAL_TEMPERATURE = 100_000.0
+_TYPICAL_VELOCITY_RTN = (-450.0, 0.0, 0.0)
+
+
+def _proton_sw_params(
+    density: float = _TYPICAL_DENSITY,
+    temperature: float = _TYPICAL_TEMPERATURE,
+) -> SolarWindParams:
+    return SolarWindParams(
+        density=density,
+        bulk_velocity_rtn=np.array(_TYPICAL_VELOCITY_RTN),
+        temperature=temperature,
+        mass=PROTON_MASS_KG,
+    )
+
+
+def _make_optimize_result(
+    jacobian: np.ndarray,
+    residuals: np.ndarray,
+    sw_params: SolarWindParams,
+) -> MagicMock:
+    """Build a minimal stand-in for `OptimizeSolarWindParamsResult`. The
+    function under test only reads `.jacobian`, `.residuals`, `.sw_params`."""
+    result = MagicMock()
+    result.jacobian = jacobian
+    result.residuals = residuals
+    result.sw_params = sw_params
+    return result
+
+
+class TestComputeHc3ParameterCovariance(unittest.TestCase):
+    """Tests for `compute_hc3_parameter_covariance`: the parameter-space-agnostic sandwich estimator shared by the proton and alpha fitters."""
+
+    def test_returns_p_by_p_symmetric_matrix_for_full_rank_jacobian(self):
+        """A full-rank (n×p) Jacobian with non-zero residuals returns a finite, symmetric (p×p) covariance."""
+        rng = np.random.default_rng(0)
+        jacobian = rng.normal(size=(30, 4))
+        residuals = rng.normal(size=30) * 0.1
+
+        cov = compute_hc3_parameter_covariance(jacobian, residuals)
+
+        self.assertEqual(cov.shape, (4, 4))
+        self.assertTrue(np.all(np.isfinite(cov)))
+        np.testing.assert_allclose(cov, cov.T, rtol=0, atol=1e-12)
+
+    def test_high_leverage_row_inflates_corresponding_diagonal(self):
+        """A single row with leverage near 1 in column j produces a much larger diagonal cov[j,j] than the same fit without the leverage spike."""
+        n_bins = 20
+        p = 3
+        # Baseline: rank-3 jacobian with no high-leverage row; uniform contribution.
+        baseline_jacobian = np.zeros((n_bins, p))
+        for i in range(n_bins):
+            baseline_jacobian[i, i % p] = 1.0
+        residuals = np.zeros(n_bins)
+        residuals[0] = 0.1
+
+        cov_baseline = compute_hc3_parameter_covariance(baseline_jacobian, residuals)
+
+        # High-leverage variant: column 0 of row 0 dominates the JᵀJ for column 0.
+        leveraged_jacobian = baseline_jacobian.copy()
+        leveraged_jacobian[0, 0] = 1000.0
+        cov_leveraged = compute_hc3_parameter_covariance(leveraged_jacobian, residuals)
+
+        self.assertGreater(cov_leveraged[0, 0], cov_baseline[0, 0] * 1e3)
+
+    def test_returns_all_nan_matrix_for_nan_jacobian(self):
+        """An all-NaN Jacobian triggers the `LinAlgError` fallback and returns a (p×p) all-NaN matrix."""
+        jacobian = np.full((20, 5), np.nan)
+        residuals = np.zeros(20)
+
+        cov = compute_hc3_parameter_covariance(jacobian, residuals)
+
+        self.assertEqual(cov.shape, (5, 5))
+        self.assertTrue(np.all(np.isnan(cov)))
+
+
+class TestDeriveUncertaintiesScalings(unittest.TestCase):
+    """Tests for `derive_uncertainties`: sanity checks on the HC3 sandwich path with a non-degenerate Jacobian."""
+
+    def setUp(self):
+        # Random 5-parameter Jacobian over 20 bins. The values themselves
+        # don't matter — we just need the matrix to have full column rank.
+        rng = np.random.default_rng(0)
+        self.jacobian = rng.normal(size=(20, N_STATE))
+        # Small-amplitude residuals so the resulting sigmas are reasonable
+        # in size; magnitude does not affect any of the scaling assertions.
+        self.residuals = rng.normal(size=20) * 0.1
+        self.sw_params = _proton_sw_params()
+        self.result = _make_optimize_result(
+            self.jacobian, self.residuals, self.sw_params
+        )
+
+    def test_returns_finite_density_and_temperature_sigmas(self):
+        """A full-rank Jacobian with non-zero residuals yields strictly positive, finite density and temperature sigmas."""
+        sigma_n, sigma_T, _ = derive_uncertainties(self.result, MagicMock())
+        self.assertTrue(np.isfinite(sigma_n))
+        self.assertGreater(sigma_n, 0.0)
+        self.assertTrue(np.isfinite(sigma_T))
+        self.assertGreater(sigma_T, 0.0)
+
+    def test_returns_finite_3x3_velocity_covariance(self):
+        """The returned velocity covariance is a finite 3x3 matrix sliced from the parameter covariance."""
+        _, _, vel_cov = derive_uncertainties(self.result, MagicMock())
+        self.assertEqual(vel_cov.shape, (3, 3))
+        self.assertTrue(np.all(np.isfinite(vel_cov)))
+
+    def test_density_sigma_scales_linearly_with_density(self):
+        """Doubling the nominal density at fixed Jacobian/residuals doubles sigma_n, pinning the `sigma_n = n * sqrt(Sigma_x[0,0])` rule."""
+        sigma_n_low, _, _ = derive_uncertainties(
+            _make_optimize_result(
+                self.jacobian, self.residuals, _proton_sw_params(density=2.0)
+            ),
+            MagicMock(),
+        )
+        sigma_n_high, _, _ = derive_uncertainties(
+            _make_optimize_result(
+                self.jacobian, self.residuals, _proton_sw_params(density=4.0)
+            ),
+            MagicMock(),
+        )
+        self.assertAlmostEqual(sigma_n_high / sigma_n_low, 2.0)
+
+    def test_temperature_sigma_scales_linearly_with_temperature(self):
+        """Tripling the nominal temperature triples sigma_T, pinning the `sigma_T = T * sqrt(Sigma_x[1,1])` rule."""
+        _, sigma_T_low, _ = derive_uncertainties(
+            _make_optimize_result(
+                self.jacobian, self.residuals, _proton_sw_params(temperature=50_000.0)
+            ),
+            MagicMock(),
+        )
+        _, sigma_T_high, _ = derive_uncertainties(
+            _make_optimize_result(
+                self.jacobian, self.residuals, _proton_sw_params(temperature=150_000.0)
+            ),
+            MagicMock(),
+        )
+        self.assertAlmostEqual(sigma_T_high / sigma_T_low, 3.0)
+
+
+class TestDeriveUncertaintiesHighLeverageBin(unittest.TestCase):
+    """Tests for `derive_uncertainties`: the `(1 - h_ii)^-2` leverage reweight at high-leverage rows."""
+
+    def test_high_leverage_row_inflates_sigma_relative_to_unweighted(self):
+        """A row with leverage near 1 inflates sigma_n by ~1e4 over the unweighted HC0 baseline, pinned against a regenerated reference value."""
+        n_bins = 20
+        jacobian = np.zeros((n_bins, N_STATE))
+        # Any value ≥ 100 produces h_00 ≈ 1; 1000 chosen for clear separation.
+        jacobian[0, LOG_DENSITY_IDX] = 1000.0
+        # Cycle through the remaining state indices on the diagonal so the
+        # Jacobian has full column rank (each state column has at least one
+        # row with a non-zero entry).
+        for i in range(1, n_bins):
+            jacobian[i, i % N_STATE] = 1.0
+        residuals = np.zeros(n_bins)
+        residuals[0] = 0.1  # only the high-leverage row carries residual
+
+        result = _make_optimize_result(jacobian, residuals, _proton_sw_params())
+        sigma_n_hc3, _, _ = derive_uncertainties(result, MagicMock())
+
+        # Hard-coded expected: HC3 sigma_n ≈ 5.0, HC0 sigma_n ≈ 5e-4 — the
+        # `(1 - h_ii)^-2` reweight at the 0.9999 leverage clip multiplies
+        # the variance by ~1e8 -> sigma ratio ≈ 1e4. Regenerated from
+        # `derive_uncertainties` on the fixture above.
+        self.assertAlmostEqual(sigma_n_hc3, 4.999985000045551, places=6)
+
+    def test_unit_leverage_row_keeps_sigma_finite(self):
+        """A row with leverage exactly 1 still produces a finite sigma_n because the implementation clips leverage at 0.9999 before the (1-h)^-2 reweight."""
+        n_bins = 5
+        jacobian = np.zeros((n_bins, N_STATE))
+        jacobian[0, 0] = 1.0
+        # zero out everything else so row 0 is the *only* row that
+        # touches column 0 — leverage of row 0 will hit exactly 1
+        residuals = np.array([0.1, 0.0, 0.0, 0.0, 0.0])
+        # remaining cols need at least *some* non-zero rows to keep pinv
+        # well-defined elsewhere; populate the rest of the diagonal
+        for i in range(1, min(n_bins, N_STATE)):
+            jacobian[i, i] = 1.0
+
+        result = _make_optimize_result(jacobian, residuals, _proton_sw_params())
+        sigma_n, _, _ = derive_uncertainties(result, MagicMock())
+        self.assertTrue(np.isfinite(sigma_n))
+
+
+class TestDeriveUncertaintiesNaNInputs(unittest.TestCase):
+    """Tests for `derive_uncertainties`: NaN-sentinel return on a degenerate Jacobian that breaks `pinv`."""
+
+    def test_nan_jacobian_returns_nan_density_temperature_and_velocity_cov(self):
+        """An all-NaN Jacobian triggers the LinAlgError fallback, returning NaN sigmas and an all-NaN 3x3 velocity covariance."""
+        jacobian = np.full((20, N_STATE), np.nan)
+        residuals = np.zeros(20)
+        result = _make_optimize_result(jacobian, residuals, _proton_sw_params())
+
+        sigma_n, sigma_T, vel_cov = derive_uncertainties(result, MagicMock())
+
+        self.assertTrue(np.isnan(sigma_n))
+        self.assertTrue(np.isnan(sigma_T))
+        self.assertEqual(vel_cov.shape, (3, 3))
+        self.assertTrue(np.all(np.isnan(vel_cov)))
+
+
+class TestMakeCorrelatedVelocity(unittest.TestCase):
+    """Tests for `make_correlated_velocity`: PSD-covariance correlated triplets vs the non-finite / non-PSD NaN-sigma fallback."""
+
+    def setUp(self):
+        self.nominal = np.array([-400.0, 5.0, -3.0])
+
+    def test_valid_covariance_returns_three_correlated_ufloats(self):
+        """A valid 3x3 PSD covariance produces a length-3 tuple of UFloats from `correlated_values`."""
+        cov = np.array(
+            [
+                [4.0, 1.0, 0.5],
+                [1.0, 2.0, 0.3],
+                [0.5, 0.3, 1.0],
+            ]
+        )
+        v = make_correlated_velocity(self.nominal, cov)
+        self.assertEqual(len(v), 3)
+
+    def test_valid_covariance_round_trips_through_uncertainties(self):
+        """The pairwise covariance reconstructed from the returned UFloats matches the input matrix and nominal values are preserved exactly."""
+        cov = np.array(
+            [
+                [4.0, 1.0, 0.5],
+                [1.0, 2.0, 0.3],
+                [0.5, 0.3, 1.0],
+            ]
+        )
+        v = make_correlated_velocity(self.nominal, cov)
+
+        recovered_cov = np.array(covariance_matrix(v))
+        np.testing.assert_allclose(recovered_cov, cov, rtol=0, atol=1e-10)
+        for component, expected in zip(v, self.nominal):
+            self.assertAlmostEqual(component.nominal_value, expected)
+
+    def test_non_finite_covariance_returns_nan_sigma_ufloats(self):
+        """An all-NaN covariance falls back to independent UFloats that preserve the nominal values but carry NaN std_devs."""
+        cov = np.full((3, 3), np.nan)
+        v = make_correlated_velocity(self.nominal, cov)
+
+        self.assertEqual(len(v), 3)
+        for component, expected in zip(v, self.nominal):
+            self.assertEqual(component.nominal_value, expected)
+            self.assertTrue(np.isnan(component.std_dev))
+
+    def test_non_finite_covariance_returns_independent_ufloats(self):
+        """The fallback UFloats from a NaN covariance are independent, so the recovered covariance matrix has no finite non-zero off-diagonals."""
+        cov = np.full((3, 3), np.nan)
+        v = make_correlated_velocity(self.nominal, cov)
+
+        # Independent UFloats have no off-diagonal correlation. Off-diagonals
+        # must be either 0 or NaN — definitely not the finite values that
+        # `correlated_values` would have produced.
+        recovered = np.array(covariance_matrix(v))
+        self.assertFalse(np.any(np.isfinite(recovered) & (recovered != 0)))
+
+    def test_negative_eigenvalue_covariance_falls_back_to_nan_sigma_ufloats(self):
+        """A finite but non-PSD covariance (diag with a negative eigenvalue) routes to the NaN-sigma fallback rather than letting `correlated_values` raise."""
+        cov = np.diag([1.0, -1.0, 1.0])
+        v = make_correlated_velocity(self.nominal, cov)
+
+        self.assertEqual(len(v), 3)
+        for component, expected in zip(v, self.nominal):
+            self.assertEqual(component.nominal_value, expected)
+            self.assertTrue(np.isnan(component.std_dev))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/science/solar_wind/test_utils.py b/tests/swapi/l3a/science/solar_wind/test_utils.py
new file mode 100644
index 000000000..5f665d551
--- /dev/null
+++ b/tests/swapi/l3a/science/solar_wind/test_utils.py
@@ -0,0 +1,110 @@
+import math
+import unittest
+
+import numpy as np
+
+from imap_l3_processing.constants import PROTON_MASS_KG
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import (
+    SolarWindParams,
+    thermal_speed,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.utils import (
+    average_spin_axis_rtn,
+    count_rate_conversion_factor,
+)
+from imap_l3_processing.swapi.response.azimuthal_transmission import (
+    AzimuthalTransmissionGrid,
+)
+from imap_l3_processing.swapi.response.swapi_response import ResponseGrid
+
+
+class TestAverageSpinAxisRtn(unittest.TestCase):
+    """Tests for `average_spin_axis_rtn`: averages the body +Y column of per-sweep RTN rotation matrices into a unit vector."""
+
+    def test_single_rotation_returns_its_y_axis(self):
+        """A single identity rotation yields +Y_RTN since column 1 of the identity is (0, 1, 0)."""
+        rotation = np.eye(3)
+        np.testing.assert_array_equal(
+            average_spin_axis_rtn(rotation[np.newaxis]), [0.0, 1.0, 0.0]
+        )
+
+    def test_constant_axis_across_sweeps_returns_that_axis(self):
+        """Five identical identity rotations average to the same +Y_RTN unit vector, confirming the mean is over the sweep axis."""
+        rotations = np.stack([np.eye(3)] * 5)
+        np.testing.assert_array_equal(
+            average_spin_axis_rtn(rotations), [0.0, 1.0, 0.0]
+        )
+
+    def test_averages_two_axes_at_pm_45_deg_to_a_unit_vector(self):
+        """Two body-+Y directions tilted ±45° in the RT-plane average to +Y_RTN after renormalization, cancelling the R components."""
+        cos45 = math.cos(math.radians(45))
+        rotation_pos = np.eye(3).copy()
+        rotation_pos[:, 1] = [cos45, cos45, 0.0]
+        rotation_neg = np.eye(3).copy()
+        rotation_neg[:, 1] = [-cos45, cos45, 0.0]
+
+        result = average_spin_axis_rtn(np.stack([rotation_pos, rotation_neg]))
+        np.testing.assert_allclose(result, [0.0, 1.0, 0.0])
+
+    def test_normalizes_average_when_input_columns_are_not_unit_length(self):
+        """Non-unit column-1 vectors still produce a length-1 output, guarding the final renormalization step against unscaled inputs."""
+        rotations = np.stack(
+            [
+                np.array([[1.0, 0.5, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
+                np.array([[1.0, 1.5, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
+            ]
+        )
+        result = average_spin_axis_rtn(rotations)
+        np.testing.assert_allclose(np.linalg.norm(result), 1.0)
+
+
+class TestCountRateConversionFactor(unittest.TestCase):
+    """Tests for `count_rate_conversion_factor`: scales the normalized forward-model integrand into a count rate in Hz."""
+
+    def _proton_params(
+        self,
+        density: float = 5.0,
+        temperature: float = 100_000.0,
+    ) -> SolarWindParams:
+        return SolarWindParams(
+            density=density,
+            bulk_velocity_rtn=np.array([-450.0, 0.0, 0.0]),
+            temperature=temperature,
+            mass=PROTON_MASS_KG,
+        )
+
+    def _response_grid(
+        self, central_effective_area: float = 0.5
+    ) -> ResponseGrid:
+        return ResponseGrid(
+            sg_passband=None,
+            oa_passband=None,
+            central_speed=float("nan"),
+            central_effective_area=central_effective_area,
+            azimuthal_transmission=AzimuthalTransmissionGrid(
+                values=np.array([]), spacing=float("nan")
+            ),
+        )
+
+    def test_matches_closed_form_formula(self):
+        """A representative proton case reproduces the analytic A_eff · n · (√(2π)σ)^-3 · (π/180)² · 1e5 product to machine precision."""
+        sw = self._proton_params(density=5.0, temperature=100_000.0)
+        rg = self._response_grid(central_effective_area=0.5)
+
+        sigma = thermal_speed(sw)
+        deg_to_rad_squared = (math.pi / 180.0) ** 2
+        km_to_cm = 1e5
+        expected = (
+            rg.central_effective_area
+            * sw.density
+            * (math.sqrt(2 * math.pi) * sigma) ** -3
+            * deg_to_rad_squared
+            * km_to_cm
+        )
+        np.testing.assert_allclose(
+            count_rate_conversion_factor(sw, rg), expected, rtol=1e-12
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/science/test_calculate_alpha_solar_wind_speed.py b/tests/swapi/l3a/science/test_calculate_alpha_solar_wind_speed.py
deleted file mode 100644
index 10123ddc6..000000000
--- a/tests/swapi/l3a/science/test_calculate_alpha_solar_wind_speed.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import math
-from pathlib import Path
-from unittest import TestCase
-
-import numpy as np
-from spacepy.pycdf import CDF
-from uncertainties import ufloat
-from uncertainties.unumpy import uarray, nominal_values, std_devs
-
-import imap_l3_processing
-from imap_l3_processing.constants import METERS_PER_KILOMETER, ALPHA_PARTICLE_CHARGE_COULOMBS, ALPHA_PARTICLE_MASS_KG
-from imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_speed import calculate_alpha_solar_wind_speed, \
-    calculate_alpha_center_of_mass, get_alpha_peak_indices, calculate_sw_speed_alpha, calculate_combined_sweeps
-from imap_l3_processing.swapi.l3a.science.speed_calculation import extract_coarse_sweep
-
-
-class TestCalculateAlphaSolarWindSpeed(TestCase):
-    def test_get_alpha_peak_indices(self):
-        test_cases = [
-            ("one clear peak", [0, 2, 3, 2, 0, 0, 0, 5, 10, 5, 0], [12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2],
-             [0, 2, 3, 2, 0]),
-            ("at edge", [3, 2, 0, 0, 0, 5, 10, 5, 0], [10, 9, 8, 7, 6, 5, 4, 3, 2], [3, 2, 0]),
-            ("wide peak", [0, 2, 3, 3, 2, 0, 0, 0, 5, 10, 5, 0], [13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2],
-             [0, 2, 3, 3, 2, 0]),
-            ("ignores values past 4*proton peak", [9, 0, 0, 2, 3, 2, 0, 0, 0, 5, 10, 5, 0],
-             [13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1],
-             [0, 2, 3, 2, 0]),
-
-        ]
-
-        for name, count_rates, energies, expected_peak_values in test_cases:
-            with self.subTest(name):
-                peak_indices = get_alpha_peak_indices(count_rates, energies)
-                extracted_peak = count_rates[peak_indices]
-                self.assertEqual(expected_peak_values, extracted_peak)
-
-    def test_an_exception_is_raised_when_no_alpha_peak(self):
-        test_cases = [
-            ("no clear peak", [0, 0, 1, 1, 1, 2, 2, 2, 10, 2, 0, 0],
-             [12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1], "Alpha peak not found"),
-            ("peak is too wide", [0, 4, 4, 4, 0, 0, 0, 0, 0, 5, 10, 10, 3, 2, 0, 0],
-             [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1], "Count rates contains multiple distinct peaks"),
-        ]
-
-        for name, count_rates, energies, exception_text in test_cases:
-            with self.subTest(name):
-                with self.assertRaises(Exception) as cm:
-                    get_alpha_peak_indices(count_rates, energies)
-                self.assertEqual(str(cm.exception), exception_text)
-
-    def test_convert_energy_to_alpha_solar_wind_speed(self):
-        test_cases = [
-            (ufloat(1000, 200), 310.562, 31.056),
-            (ufloat(1300, 10), 354.096, 1.362),
-            (ufloat(1800, 5), 416.663, 0.579),
-            (ufloat(2500, 600), 491.042, 58.925),
-            (ufloat(5000, 25), 694.439, 1.736)
-        ]
-
-        for energy, expected_speed, expected_uncertainty in test_cases:
-            with self.subTest(f"converting energy of {energy} to speed"):
-                alpha_sw_speed = calculate_sw_speed_alpha(energy)
-
-                self.assertAlmostEqual(expected_speed, alpha_sw_speed.n, 3)
-                self.assertAlmostEqual(expected_uncertainty, alpha_sw_speed.s, 3)
-
-    def test_calculate_alpha_solar_wind_speed_from_file(self):
-        file_path = Path(
-            imap_l3_processing.__file__).parent.parent / 'tests' / 'test_data' / 'swapi' / 'imap_swapi_l2_fake-menlo-5-sweeps_20100101_v002.cdf'
-        with CDF(str(file_path)) as cdf:
-            energy = cdf["esa_energy"][...]
-            count_rate = cdf["swp_coin_rate"][...]
-            count_rate_delta = cdf["swp_coin_unc"][...]
-
-        alpha_solar_wind_speed = calculate_alpha_solar_wind_speed(uarray(count_rate, count_rate_delta), energy)
-
-        self.assertAlmostEqual(496.490, alpha_solar_wind_speed.nominal_value, 3)
-        self.assertAlmostEqual(2.811, alpha_solar_wind_speed.std_dev, 3)
-
-    def test_calculate_alpha_center_of_mass_with_fake_data(self):
-        test_cases = [
-            (925, 1850, 50),
-            (950, 1900, 51),
-            (1000, 2000, 54),
-            (1050, 2100, 56),
-            (1100, 2200, 59)
-        ]
-        for modeled_proton_peak_energy, modeled_alpha_particle_peak_energy, expected_uncertainty in test_cases:
-            with self.subTest(f"alpha particle peak energy is: {modeled_alpha_particle_peak_energy}"):
-                count_rates, energies = generate_sweep_data(modeled_proton_peak_energy,
-                                                            modeled_alpha_particle_peak_energy)
-                count_rate_delta = synthesize_uncertainties(count_rates)
-
-                alpha_energy_center_of_mass = calculate_alpha_center_of_mass(uarray(count_rates, count_rate_delta),
-                                                                             energies)
-
-                self.assertAlmostEqual(modeled_alpha_particle_peak_energy, alpha_energy_center_of_mass.nominal_value,
-                                       delta=5)
-                self.assertAlmostEqual(expected_uncertainty, alpha_energy_center_of_mass.std_dev, 0)
-
-    def test_calculate_alpha_solar_wind_speed_basic(self):
-        energies = np.array([[0, 1000, 700, 600, 500, 400, 300, 200, 100, 50]])
-
-        count_rates = np.array([[0, 13,  14, 15, 14, 13, 16, 18, 14, 0]])
-
-        count_rates_with_uncertainties = uarray(count_rates, np.full_like(count_rates, 1.0))
-
-        speed = calculate_alpha_solar_wind_speed(count_rates_with_uncertainties, energies)
-
-        expected_speed = math.sqrt(2 * 600 * ALPHA_PARTICLE_CHARGE_COULOMBS / ALPHA_PARTICLE_MASS_KG) / METERS_PER_KILOMETER
-        self.assertAlmostEqual(expected_speed, speed.n, 0)
-
-
-    def test_calculate_alpha_solar_wind_speed_excludes_zeros(self):
-        energies = np.array([[0, 1000, 700, 650, 600, 550, 500, 450, 400, 300, 200, 100, 50]])
-        count_rates = np.array([[0, 13,  0, 5, 10, 15, 0, 10, 8, 16, 18, 14, 0]])
-
-        count_rates_with_uncertainties = uarray(count_rates, np.full_like(count_rates, 1.0))
-
-        speed = calculate_alpha_solar_wind_speed(count_rates_with_uncertainties, energies)
-
-        expected_speed = math.sqrt(2 * 550 * ALPHA_PARTICLE_CHARGE_COULOMBS / ALPHA_PARTICLE_MASS_KG) / METERS_PER_KILOMETER
-        self.assertAlmostEqual(expected_speed, speed.n, 0)
-
-    def test_calculate_alpha_solar_wind_speed_raises_error_if_fewer_than_three_count_rates_nonzero(self):
-        energies = np.array([[0, 1000, 700, 650, 600, 550, 500, 450, 400, 300, 200, 100, 50]])
-        count_rates = np.array([[0, 0,  0, 0, 0, 21, 10, 0, 0, 0, 30, 0, 0]])
-
-        count_rates_with_uncertainties = uarray(count_rates, np.full_like(count_rates, 1.0))
-
-        with self.assertRaises(Exception):
-            calculate_alpha_solar_wind_speed(count_rates_with_uncertainties, energies)
-
-    def test_calculate_combined_sweeps(self):
-        file_path = Path(
-            imap_l3_processing.__file__).parent.parent / 'tests' / 'test_data' / 'swapi' / 'imap_swapi_l2_fake-menlo-5-sweeps_20100101_v002.cdf'
-        with CDF(str(file_path)) as cdf:
-            energy = cdf["esa_energy"][...]
-            count_rate = cdf["swp_coin_rate"][...]
-            count_rate_delta = cdf["swp_coin_unc"][...]
-
-        actual_averaged_count_rates, actual_energy = calculate_combined_sweeps(uarray(count_rate, count_rate_delta),
-                                                                               energy)
-        expected_avg_count_rates = np.array([ufloat(15.2, 4.2708313008125245), ufloat(25.8, 5.5641710972974225),
-                                             ufloat(26.0, 5.585696017507577), ufloat(15.4, 4.298837052040936),
-                                             ufloat(5.8, 2.638181191654584)])
-
-        np.testing.assert_array_equal(nominal_values(actual_averaged_count_rates[22:27]),
-                                      nominal_values(expected_avg_count_rates))
-        np.testing.assert_array_equal(std_devs(actual_averaged_count_rates[22:27]), std_devs(expected_avg_count_rates))
-        np.testing.assert_array_equal(actual_energy[0:10],
-                                      np.array([19098, 17541, 16113, 14798, 13591, 12485, 11467, 10532, 9675,
-                                                8885]))
-
-    def test_extract_coarse_sweep(self):
-        first_sweep_energies = [i for i in range(64)]
-        second_sweep_energies = [i * 2 for i in range(64)]
-        two_sweeps_energies = np.array([first_sweep_energies, second_sweep_energies])
-
-        actual_energies = extract_coarse_sweep(two_sweeps_energies)
-
-        np.testing.assert_array_equal(first_sweep_energies[1:63], actual_energies[0])
-        np.testing.assert_array_equal(second_sweep_energies[1:63], actual_energies[1])
-
-
-def get_sweep_voltages(sweep_table_id=0):
-    energies = np.geomspace(19000, 100, 62)
-    return energies / get_k_factor()
-
-
-def synthesize_uncertainties(count_rates):
-    return np.sqrt(6 * count_rates)
-
-
-def generate_sweep_data(proton_center, alpha_center) -> tuple[np.ndarray, np.ndarray]:
-    voltages = get_sweep_voltages()
-    energies = voltages * get_k_factor()
-    background = 0.1
-    proton_peak = generate_peak(energies, 1200, proton_center, 100)
-    alpha_peak = generate_peak(energies, 20, alpha_center, 100)
-
-    return (proton_peak + alpha_peak + background, energies)
-
-
-def get_k_factor():
-    return 1.8
-
-
-def generate_peak(energies, height, center, narrowness):
-    return np.exp(np.log(height) - narrowness * np.square(np.log(energies) - np.log(center)))
diff --git a/tests/swapi/l3a/science/test_calculate_alpha_solar_wind_temperature_and_density.py b/tests/swapi/l3a/science/test_calculate_alpha_solar_wind_temperature_and_density.py
deleted file mode 100644
index 1b86541d7..000000000
--- a/tests/swapi/l3a/science/test_calculate_alpha_solar_wind_temperature_and_density.py
+++ /dev/null
@@ -1,142 +0,0 @@
-from contextlib import contextmanager
-from pathlib import Path
-from unittest import TestCase
-from unittest.mock import patch, Mock
-
-import numpy as np
-from spacepy.pycdf import CDF
-from uncertainties import ufloat
-from uncertainties.unumpy import uarray
-
-import imap_l3_processing
-from imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_temperature_and_density import \
-    AlphaTemperatureDensityCalibrationTable, calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps
-from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
-from tests.test_helpers import get_test_data_path
-
-
-class TestCalculateAlphaSolarWindTemperatureAndDensity(TestCase):
-    def setUp(self) -> None:
-        data_file_path = get_test_data_path("swapi/imap_swapi_l2_fake-menlo-5-sweeps_20100101_v002.cdf")
-        with CDF(str(data_file_path)) as cdf:
-            self.energy = cdf["esa_energy"][...]
-            self.count_rate = cdf["swp_coin_rate"][...]
-            self.count_rate_delta = cdf["swp_coin_unc"][...]
-
-        lookup_table_file_path = get_test_data_path("swapi/imap_swapi_alpha-density-temperature-lut_20240920_v000.dat")
-        self.calibration_table = AlphaTemperatureDensityCalibrationTable.from_file(lookup_table_file_path)
-
-    def test_temperature_and_density_calibration_table_from_file(self):
-        file_path = Path(
-            imap_l3_processing.__file__).parent.parent / 'tests' / 'test_data' / 'swapi' / "imap_swapi_alpha-density-temperature-lut_20240920_v000.dat"
-
-        calibration_table = AlphaTemperatureDensityCalibrationTable.from_file(file_path)
-
-        sw_speed = ufloat(460, 10)
-        density = ufloat(0.25, 0.03)
-        temperature = ufloat(6.0000e+05, 100)
-
-        self.assertAlmostEqual(0.39999999,
-                               calibration_table.lookup_density(sw_speed, density, temperature).nominal_value)
-        self.assertAlmostEqual(7.2e+05,
-                               calibration_table.lookup_temperature(sw_speed, density, temperature).nominal_value)
-
-    def test_calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps(self):
-        speed = ufloat(496.490, 2.811)
-
-        efficiency = 0.08
-
-        actual_alpha_sw_temp_and_density = calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps(
-            self.calibration_table, speed,
-            uarray(self.count_rate, self.count_rate_delta), self.energy, efficiency)
-
-        np.testing.assert_allclose(493916.041942, actual_alpha_sw_temp_and_density.temperature.nominal_value)
-        np.testing.assert_allclose(155210.29054, actual_alpha_sw_temp_and_density.temperature.std_dev)
-        np.testing.assert_allclose(3.8704e-3 / efficiency, actual_alpha_sw_temp_and_density.density.nominal_value,
-                                   rtol=1e-4)
-        np.testing.assert_allclose(4.9177e-4 / efficiency, actual_alpha_sw_temp_and_density.density.std_dev, rtol=1e-4)
-        self.assertEqual(SwapiL3Flags.NONE, actual_alpha_sw_temp_and_density.bad_fit_flag)
-        np.testing.assert_allclose(411596.701618, actual_alpha_sw_temp_and_density.pre_lut_temperature.nominal_value)
-        np.testing.assert_allclose(129341.909538, actual_alpha_sw_temp_and_density.pre_lut_temperature.std_dev)
-        np.testing.assert_allclose(0.030238, actual_alpha_sw_temp_and_density.pre_lut_density.nominal_value,
-                                   rtol=1e-4)
-        np.testing.assert_allclose(0.003842, actual_alpha_sw_temp_and_density.pre_lut_density.std_dev, rtol=1e-4)
-
-    def test_calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps_returns_pre_lookup_values(self):
-        speed = ufloat(496.490, 2.811)
-
-        efficiency = 0.08
-        mock_calibration_table = Mock()
-
-        actual_alpha_sw_temp_and_density = calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps(
-            mock_calibration_table, speed,
-            uarray(self.count_rate, self.count_rate_delta), self.energy, efficiency)
-
-        self.assertEqual(mock_calibration_table.lookup_density.call_args.args[1],
-                         actual_alpha_sw_temp_and_density.pre_lut_density)
-        self.assertEqual(mock_calibration_table.lookup_density.call_args.args[2],
-                         actual_alpha_sw_temp_and_density.pre_lut_temperature)
-        self.assertEqual(mock_calibration_table.lookup_temperature.call_args.args[1],
-                         actual_alpha_sw_temp_and_density.pre_lut_density)
-        self.assertEqual(mock_calibration_table.lookup_temperature.call_args.args[2],
-                         actual_alpha_sw_temp_and_density.pre_lut_temperature)
-
-    @patch(
-        'imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_temperature_and_density.get_alpha_peak_indices')
-    @patch(
-        'imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_temperature_and_density.calculate_combined_sweeps')
-    def test_raises_error_when_chi_squared_over_ten(self, mock_calculate_combine_sweeps, mock_get_alpha_peak_indices):
-        speed = ufloat(496.490, 2.811)
-        peak_energies = np.array([2944, 2705, 2485, 2281, 2094])
-        peak_coincidence_rates = np.array([
-            ufloat(20.2, 4.2708313008125245),
-            ufloat(10.8, 5.5641710972974225),
-            ufloat(606.0, 5.585696017507577),
-            ufloat(10.4, 4.298837052040936),
-            ufloat(5.8, 2.638181191654584),
-        ])
-        efficiency = 0.7
-
-        mock_calculate_combine_sweeps.return_value = peak_coincidence_rates, peak_energies
-        mock_get_alpha_peak_indices.return_value = slice(0, 5)
-
-        actual_alpha_sw_temp_and_density = calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps(
-            self.calibration_table, speed,
-            uarray(self.count_rate,
-                   self.count_rate_delta),
-            self.energy, efficiency)
-
-        np.isnan(actual_alpha_sw_temp_and_density.temperature.nominal_value)
-        np.isnan(actual_alpha_sw_temp_and_density.temperature.std_dev)
-        np.isnan(actual_alpha_sw_temp_and_density.density.nominal_value)
-        np.isnan(actual_alpha_sw_temp_and_density.density.std_dev)
-        self.assertEqual(SwapiL3Flags.HI_CHI_SQ, actual_alpha_sw_temp_and_density.bad_fit_flag)
-
-    @patch(
-        'imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_temperature_and_density.get_alpha_peak_indices')
-    @patch(
-        'imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_temperature_and_density.calculate_combined_sweeps')
-    def test_excludes_zero_count_rates_from_fit(self, mock_calculate_combine_sweeps, mock_get_alpha_peak_indices):
-        speed = ufloat(496.490, 2.811)
-        peak_energies = np.array([2944, 2705, 2485, 2281, 2094])
-        peak_coincidence_rates = np.array([
-            ufloat(15.2, 4.2708313008125245),
-            ufloat(30.8, 5.5641710972974225),
-            ufloat(0, 5.585696017507577),
-            ufloat(30.4, 4.298837052040936),
-            ufloat(8.8, 2.638181191654584),
-        ]) * 10
-        efficiency = 0.7
-
-        mock_calculate_combine_sweeps.return_value = peak_coincidence_rates, peak_energies
-        mock_get_alpha_peak_indices.return_value = slice(0, 5)
-        with assert_does_not_error():
-            calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps(self.calibration_table, speed,
-                                                                                   uarray(self.count_rate,
-                                                                                          self.count_rate_delta),
-                                                                                   self.energy, efficiency)
-
-
-@contextmanager
-def assert_does_not_error():
-    yield
diff --git a/tests/swapi/l3a/science/test_calculate_pickup_ion.py b/tests/swapi/l3a/science/test_calculate_pickup_ion.py
index 17c5797b8..6dca96d9d 100644
--- a/tests/swapi/l3a/science/test_calculate_pickup_ion.py
+++ b/tests/swapi/l3a/science/test_calculate_pickup_ion.py
@@ -13,13 +13,12 @@
 from imap_l3_processing.constants import PROTON_MASS_KG, \
     PROTON_CHARGE_COULOMBS, ONE_AU_IN_KM, ONE_SECOND_IN_NANOSECONDS, \
     HE_PUI_PARTICLE_MASS_KG, BOLTZMANN_CONSTANT_JOULES_PER_KELVIN
-from imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_speed import calculate_combined_sweeps
 from imap_l3_processing.swapi.l3a.science.calculate_pickup_ion import calculate_pui_energy_cutoff, \
     extract_pui_energy_bins, \
     convert_velocity_relative_to_imap, calculate_velocity_vector, FittingParameters, \
     ForwardModel, convert_velocity_to_reference_frame, model_count_rate_integral, \
     calculate_pickup_ion_values, ModelCountRateCalculator, calculate_ten_minute_velocities, \
-    calculate_pui_velocity_vector, calculate_solar_wind_velocity_vector, calculate_helium_pui_density, \
+    calculate_pui_velocity_vector, calculate_helium_pui_density, \
     calculate_helium_pui_temperature, calc_chi_squared_lm_fit
 from imap_l3_processing.swapi.l3a.science.density_of_neutral_helium_lookup_table import \
     DensityOfNeutralHeliumLookupTable
@@ -371,8 +370,7 @@ def test_model_count_rate_integral(self, mock_calculate_speed, mock_forward_mode
     @patch(f"{MODULE}.calculate_pui_energy_cutoff")
     @patch(f"{MODULE}.lmfit.Minimizer")
     @patch(f"{MODULE}.spiceypy")
-    @patch(f"{MODULE}.calculate_combined_sweeps")
-    def test_calculate_pickup_ions_with_minimize_mocked(self, mock_calculate_combined_sweeps, mock_spice, mock_minimizer_constructor,
+    def test_calculate_pickup_ions_with_minimize_mocked(self, mock_spice, mock_minimizer_constructor,
                                                         mock_calculate_pui_energy_cutoff, mock_extract_pui_energy_bins):
         ephemeris_time_for_epoch = 100000
         mock_spice.unitim.return_value = ephemeris_time_for_epoch
@@ -389,8 +387,8 @@ def test_calculate_pickup_ions_with_minimize_mocked(self, mock_calculate_combine
             energy = cdf["esa_energy"][...]
             count_rate = cdf["swp_coin_rate"][...]
 
-            combined_counts, combined_energies = calculate_combined_sweeps(count_rate, energy)
-            mock_calculate_combined_sweeps.return_value = combined_counts, combined_energies
+            combined_counts = np.mean(count_rate[:, 1:63], axis=0)
+            combined_energies = np.mean(energy[:, 1:63], axis=0)
             extracted_counts = [1.9, 1.2, 0]
             extracted_energies = [5000, 4000, 3000]
             extracted_indices = [4, 3, 2]
@@ -427,18 +425,16 @@ def test_calculate_pickup_ions_with_minimize_mocked(self, mock_calculate_combine
                                                                     self.density_of_neutral_helium_lookup_table,
                                                                     efficiency_lut, h_inflow_vector, he_inflow_vector)
 
-            mock_calculate_combined_sweeps.assert_called_once_with(count_rate, energy)
-
             mock_spice.unitim.assert_called_with(input_epochs / 1e9, "TT", "ET")
             mock_calculate_pui_energy_cutoff.assert_has_calls([
                 call(PROTON_MASS_KG, ephemeris_time_for_epoch, sw_velocity, h_inflow_vector),
                 call(HE_PUI_PARTICLE_MASS_KG, ephemeris_time_for_epoch, sw_velocity, he_inflow_vector)])
-            mock_extract_pui_energy_bins.assert_called_with(range(62, 0, -1),
-                                                            combined_energies,
-                                                            combined_counts,
-                                                            lower_energy_cutoff * 1.25,
-                                                            upper_energy_cutoff * 1.2,
-                                                            )
+            extract_call_args = mock_extract_pui_energy_bins.call_args.args
+            self.assertEqual(range(62, 0, -1), extract_call_args[0])
+            np.testing.assert_array_equal(combined_energies, extract_call_args[1])
+            np.testing.assert_array_equal(combined_counts, extract_call_args[2])
+            self.assertEqual(lower_energy_cutoff * 1.25, extract_call_args[3])
+            self.assertEqual(upper_energy_cutoff * 1.2, extract_call_args[4])
             actual_count_rates, indices, model_count_rates_calculator, ephemeris_time, sweep_count = \
                 mock_minimizer_constructor.call_args.kwargs['fcn_args']
             self.assertEqual(calc_chi_squared_lm_fit, mock_minimizer_constructor.call_args.args[0])
@@ -491,11 +487,9 @@ def _transform_simplex_vertex(vertex):
     @patch(f"{MODULE}.calculate_pui_energy_cutoff")
     @patch(f"{MODULE}.extract_pui_energy_bins")
     @patch(f"{MODULE}.lmfit.Minimizer")
-    @patch(f"{MODULE}.calculate_combined_sweeps")
-    def test_calculate_pickup_ion_bad_fit(self, mock_calculate_combined_sweeps, mock_minimizer_class,
+    def test_calculate_pickup_ion_bad_fit(self, mock_minimizer_class,
                                           mock_extract_pui_energy_bins, _, __):
 
-        mock_calculate_combined_sweeps.return_value = (np.array([1]), np.array([1]))
         mock_extract_pui_energy_bins.return_value = (np.array([1]), np.array([1]), np.array([1]))
 
         fit_params = {
@@ -511,13 +505,13 @@ def test_calculate_pickup_ion_bad_fit(self, mock_calculate_combined_sweeps, mock
 
         actual_fitting_params = calculate_pickup_ion_values(instrument_response_lookup_table=Mock(),
                                                             geometric_factor_calibration_table=Mock(),
-                                                            energy=np.array([1]), count_rates=np.array([1]),
+                                                            energy=np.zeros((1, 72)), count_rates=np.zeros((1, 72)),
                                                             center_of_epoch=1e9, sw_velocity_vector=np.array([1, 0, 0]),
                                                             density_of_neutral_helium_lookup_table=Mock(),
                                                             efficiency_table=Mock(), hydrogen_inflow_vector=Mock(),
                                                             helium_inflow_vector=Mock())
 
-        self.assertEqual(actual_fitting_params.flags, SwapiL3Flags.HI_CHI_SQ)
+        self.assertEqual(actual_fitting_params.flags, SwapiL3Flags.BAD_FIT)
         self.assertEqual(actual_fitting_params.cooling_index, fit_params["cooling_index"])
         self.assertEqual(actual_fitting_params.cutoff_speed, fit_params["cutoff_speed"])
         self.assertEqual(actual_fitting_params.ionization_rate, fit_params["ionization_rate"])
@@ -748,103 +742,78 @@ def test_snapshot_model_count_rate_result(self):
                                            0.6198514811899901, 0.5685065780319507, 0.5197560948020243],
                                    rtol=1e-12)
 
-    @patch(f"{MODULE}.calculate_solar_wind_velocity_vector")
-    def test_calculate_ten_minute_velocities(self, mock_calculate_solar_wind_velocity_vector):
+    def test_calculate_ten_minute_velocities(self):
+        """Velocities split into 10-chunk windows and averaged per window; the trailing partial window is averaged across its remaining members."""
         x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21])
         y = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180,
                       190, 200, 210])
         z = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180,
                       190, 200, 210])
 
+        velocity_vectors_dps = np.transpose([x, y, z])
         one_min_quality_flags = np.repeat(SwapiL3Flags.NONE, 21)
 
-        mock_calculate_solar_wind_velocity_vector.return_value = np.transpose([x, y, z])
-
-        mock_speed = Mock()
-        mock_deflection_angles = Mock()
-        mock_clock_angles = Mock()
-        averaged_velocities, actual_quality_flags = calculate_ten_minute_velocities(mock_speed, mock_deflection_angles,
-                                                                                    mock_clock_angles,
-                                                                                    one_min_quality_flags)
+        averaged_velocities, actual_quality_flags = calculate_ten_minute_velocities(
+            velocity_vectors_dps, one_min_quality_flags
+        )
 
         expected_averaged_velocities = np.array([[5.5, 55, 55], [15.5, 155, 155], [21, 210, 210]])
-
         expected_quality_flags = np.repeat(SwapiL3Flags.NONE, 3)
 
-        mock_calculate_solar_wind_velocity_vector.assert_called_with(mock_speed, mock_deflection_angles,
-                                                                     mock_clock_angles)
-
         np.testing.assert_array_equal(averaged_velocities, expected_averaged_velocities)
         np.testing.assert_array_equal(actual_quality_flags, expected_quality_flags)
 
-    @patch(f"{MODULE}.calculate_solar_wind_velocity_vector")
-    def test_calculate_ten_minute_velocities_with_swapi_quality_flag(self, mock_calculate_solar_wind_velocity_vector):
+    def test_calculate_ten_minute_velocities_with_swapi_quality_flag(self):
+        """A single per-minute quality flag bits-OR's into the 10-minute window that contains it."""
         x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21])
         y = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180,
                       190, 200, 210])
         z = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180,
                       190, 200, 210])
 
+        velocity_vectors_dps = np.transpose([x, y, z])
         one_min_quality_flags = np.repeat(SwapiL3Flags.NONE, 21)
-        one_min_quality_flags[13] = SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED
-
-        mock_calculate_solar_wind_velocity_vector.return_value = np.transpose([x, y, z])
+        one_min_quality_flags[13] = SwapiL3Flags.FIT_ERROR
 
-        mock_speed = Mock()
-        mock_deflection_angles = Mock()
-        mock_clock_angles = Mock()
-        averaged_velocities, actual_quality_flags = calculate_ten_minute_velocities(mock_speed, mock_deflection_angles,
-                                                                                    mock_clock_angles,
-                                                                                    one_min_quality_flags)
+        averaged_velocities, actual_quality_flags = calculate_ten_minute_velocities(
+            velocity_vectors_dps, one_min_quality_flags
+        )
 
         expected_averaged_velocities = np.array([[5.5, 55, 55], [15.5, 155, 155], [21, 210, 210]])
         expected_quality_flags = np.array([
             SwapiL3Flags.NONE,
-            SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED,
+            SwapiL3Flags.FIT_ERROR,
             SwapiL3Flags.NONE,
         ])
 
-        mock_calculate_solar_wind_velocity_vector.assert_called_with(mock_speed, mock_deflection_angles,
-                                                                     mock_clock_angles)
-
         np.testing.assert_array_equal(averaged_velocities, expected_averaged_velocities)
         np.testing.assert_array_equal(actual_quality_flags, expected_quality_flags)
 
-    @patch(f"{MODULE}.calculate_solar_wind_velocity_vector")
-    def test_calculate_ten_minute_velocities_with_multiple_quality_flag(self,
-                                                                        mock_calculate_solar_wind_velocity_vector):
+    def test_calculate_ten_minute_velocities_with_multiple_quality_flag(self):
+        """Multiple distinct per-minute quality flags inside the same 10-minute window OR together into a combined bitmask for that window."""
         x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21])
         y = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180,
                       190, 200, 210])
         z = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180,
                       190, 200, 210])
 
-        OTHER_QUALITY_FLAG = 2 ** 3
+        OTHER_QUALITY_FLAG = 2 ** 4
+        velocity_vectors_dps = np.transpose([x, y, z])
         one_min_quality_flags = np.repeat(SwapiL3Flags.NONE, 21)
-
-        one_min_quality_flags[13] = SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED
+        one_min_quality_flags[13] = SwapiL3Flags.FIT_ERROR
         one_min_quality_flags[14] = OTHER_QUALITY_FLAG
 
-        mock_calculate_solar_wind_velocity_vector.return_value = np.transpose([x, y, z])
-
-        mock_speed = Mock()
-        mock_deflection_angles = Mock()
-        mock_clock_angles = Mock()
-        averaged_velocities, actual_quality_flags = calculate_ten_minute_velocities(mock_speed,
-                                                                                    mock_deflection_angles,
-                                                                                    mock_clock_angles,
-                                                                                    one_min_quality_flags)
+        averaged_velocities, actual_quality_flags = calculate_ten_minute_velocities(
+            velocity_vectors_dps, one_min_quality_flags
+        )
 
         expected_averaged_velocities = np.array([[5.5, 55, 55], [15.5, 155, 155], [21, 210, 210]])
         expected_quality_flags = np.array([
             SwapiL3Flags.NONE,
-            SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED | OTHER_QUALITY_FLAG,
+            SwapiL3Flags.FIT_ERROR | OTHER_QUALITY_FLAG,
             SwapiL3Flags.NONE,
         ])
 
-        mock_calculate_solar_wind_velocity_vector.assert_called_with(mock_speed, mock_deflection_angles,
-                                                                     mock_clock_angles)
-
         np.testing.assert_array_equal(averaged_velocities, expected_averaged_velocities)
         np.testing.assert_array_equal(actual_quality_flags, expected_quality_flags)
 
@@ -859,17 +828,6 @@ def test_calculate_pickup_ion_velocities(self):
 
         np.testing.assert_array_almost_equal(expected_values, actual, 1)
 
-    def test_calculate_solar_wind_velocity_vector(self):
-        speed = np.array([400, 390, 400, 410, 400, 400])
-        deflection_angle = np.array([90, 0, 180, 0, 90, 90])
-        clock_angle = np.array([-270, -180, -90, 0, 0, -180])
-
-        actual = calculate_solar_wind_velocity_vector(speed, deflection_angle, clock_angle)
-
-        expected_values = [(0, -400, 0), (0, 0, -390), (0, 0, 400), (0, 0, -410), (-400, 0, 0), (400, 0, 0)]
-
-        np.testing.assert_array_almost_equal(actual, expected_values, 1)
-
     def test_calc_chi_squared_lm_fit(self):
         params = MagicMock()
         observed_count_rates = np.array([2, 20, 200, 2000])
@@ -891,14 +849,12 @@ def test_calc_chi_squared_lm_fit(self):
     @patch(f"{MODULE}.calculate_pui_energy_cutoff")
     @patch(f"{MODULE}.extract_pui_energy_bins")
     @patch(f"{MODULE}.lmfit.Minimizer")
-    @patch(f"{MODULE}.calculate_combined_sweeps")
     def test_calculate_pickup_ion_computes_partial_uncertainties(
-            self, mock_calculate_combined_sweeps, mock_minimizer_class,
+            self, mock_minimizer_class,
                                           mock_extract_pui_energy_bins, _, __,
             mock_hessian, mock_inv,
     ):
 
-        mock_calculate_combined_sweeps.return_value = (np.array([1]), np.array([1]))
         mock_extract_pui_energy_bins.return_value = (np.array([1]), np.array([1]), np.array([1]))
 
         fit_params = {
@@ -938,7 +894,7 @@ def test_calculate_pickup_ion_computes_partial_uncertainties(
 
         actual_fitting_params = calculate_pickup_ion_values(instrument_response_lookup_table=Mock(),
                                                             geometric_factor_calibration_table=Mock(),
-                                                            energy=np.array([1]), count_rates=np.array([1]),
+                                                            energy=np.zeros((1, 72)), count_rates=np.zeros((1, 72)),
                                                             center_of_epoch=1e9, sw_velocity_vector=np.array([1, 0, 0]),
                                                             density_of_neutral_helium_lookup_table=Mock(),
                                                             efficiency_table=Mock(), hydrogen_inflow_vector=Mock(),
@@ -965,14 +921,12 @@ def test_calculate_pickup_ion_computes_partial_uncertainties(
     @patch(f"{MODULE}.calculate_pui_energy_cutoff")
     @patch(f"{MODULE}.extract_pui_energy_bins")
     @patch(f"{MODULE}.lmfit.Minimizer")
-    @patch(f"{MODULE}.calculate_combined_sweeps")
     def test_returns_nominal_values_when_uncertainty_fallback_fails(
-            self, mock_calculate_combined_sweeps, mock_minimizer_class,
+            self, mock_minimizer_class,
                                           mock_extract_pui_energy_bins, _, __,
             mock_hessian, mock_inv,
     ):
 
-        mock_calculate_combined_sweeps.return_value = (np.array([1]), np.array([1]))
         mock_extract_pui_energy_bins.return_value = (np.array([1]), np.array([1]), np.array([1]))
 
         fit_params = {
@@ -1021,7 +975,7 @@ def test_returns_nominal_values_when_uncertainty_fallback_fails(
 
                 actual_fitting_params = calculate_pickup_ion_values(instrument_response_lookup_table=Mock(),
                                                                     geometric_factor_calibration_table=Mock(),
-                                                                    energy=np.array([1]), count_rates=np.array([1]),
+                                                                    energy=np.zeros((1, 72)), count_rates=np.zeros((1, 72)),
                                                                     center_of_epoch=1e9,
                                                                     sw_velocity_vector=np.array([1, 0, 0]),
                                                                     density_of_neutral_helium_lookup_table=Mock(),
diff --git a/tests/swapi/l3a/science/test_calculate_proton_solar_wind_clock_and_deflection_angles.py b/tests/swapi/l3a/science/test_calculate_proton_solar_wind_clock_and_deflection_angles.py
deleted file mode 100644
index d07968d38..000000000
--- a/tests/swapi/l3a/science/test_calculate_proton_solar_wind_clock_and_deflection_angles.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from pathlib import Path
-from unittest import TestCase
-from unittest.mock import patch, sentinel
-
-from uncertainties import ufloat
-
-import imap_l3_processing
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_clock_and_deflection_angles import \
-    calculate_clock_angle, ClockAngleCalibrationTable, calculate_deflection_angle
-
-
-class TestCalculateProtonSolarWindClockAndDeflectionAngles(TestCase):
-    def setUp(self):
-        file_path = Path(
-            imap_l3_processing.__file__).parent.parent / 'tests' / 'test_data' / 'swapi' / 'imap_swapi_clock-angle-and-flow-deflection-lut_20240918_v000.dat'
-        self.lookup_table = ClockAngleCalibrationTable.from_file(file_path)
-
-    def test_calculate_clock_angle(self):
-        proton_solar_wind_speed, a, phi, b = (
-            ufloat(405.00, .1), ufloat(15.0, .1), ufloat(33.3, .1),
-            ufloat(1_000.0, .1))
-        clock_angle = calculate_clock_angle(self.lookup_table, proton_solar_wind_speed, a, phi, b)
-        self.assertAlmostEqual(phi.n - 6.09, clock_angle.n, 4)
-        self.assertAlmostEqual(0.1, clock_angle.s, 4)
-
-    @patch(
-        'imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_clock_and_deflection_angles.estimate_deflection_and_clock_angles')
-    def test_calculate_clock_angle_out_of_lut_range(self, mock_estimate_deflection_and_clock_angles):
-        proton_solar_wind_speed, a, phi, b = (ufloat(350, .1),
-                                              ufloat(500, .2),
-                                              ufloat(4.69, .2),
-                                              ufloat(1_000.0, .2))
-
-        mock_estimate_deflection_and_clock_angles.return_value = (sentinel.deflection_angle, sentinel.clock_angle)
-        clock_angle = calculate_clock_angle(self.lookup_table, proton_solar_wind_speed, a, phi, b)
-
-        mock_estimate_deflection_and_clock_angles.assert_called_with(proton_solar_wind_speed.nominal_value)
-        self.assertAlmostEqual(sentinel.clock_angle, clock_angle)
-
-    def test_calculate_clock_angle_with_interpolation(self):
-        proton_solar_wind_speed, a, phi, b = (ufloat(845.00, .1),
-                                              ufloat(31.25, .2),
-                                              ufloat(33.3, .2),
-                                              ufloat(1_000.0, .2))
-        clock_angle = calculate_clock_angle(self.lookup_table, proton_solar_wind_speed, a, phi, b)
-        self.assertAlmostEqual(phi.n - 6.39, clock_angle.n, 4)
-        self.assertAlmostEqual(phi.s, clock_angle.s, 3)
-
-    def test_calculate_clock_angle_mods(self):
-        proton_solar_wind_speed, a, phi, b = (ufloat(845.00, .1),
-                                              ufloat(31.25, .2),
-                                              ufloat(4.69, .2),
-                                              ufloat(1_000.0, .2))
-        clock_angle = calculate_clock_angle(self.lookup_table, proton_solar_wind_speed, a, phi, b)
-        self.assertAlmostEqual(358.3, clock_angle.n, 3)
-        self.assertAlmostEqual(phi.s, clock_angle.s, 3)
-
-    def test_calculate_deflection_angle_with_interpolation(self):
-        proton_solar_wind_speed, a, phi, b = (ufloat(845.00, .1),
-                                              ufloat(31.25, .2),
-                                              ufloat(33.3, .2),
-                                              ufloat(1_000.0, .2))
-        deflection_angle = calculate_deflection_angle(self.lookup_table, proton_solar_wind_speed, a, phi, b)
-        self.assertAlmostEqual(3.125, deflection_angle.n, 4)
-        self.assertAlmostEqual(0.02, deflection_angle.s, 4)
-
-    def test_calculate_deflection_angle_out_of_lut_range(self):
-        proton_solar_wind_speed, a, phi, b = (ufloat(350, .1),
-                                              ufloat(500, .2),
-                                              ufloat(4.69, .2),
-                                              ufloat(1_000.0, .2))
-
-        actual_deflection_angle = calculate_deflection_angle(self.lookup_table, proton_solar_wind_speed, a, phi, b)
-
-        self.assertEqual(5.0, actual_deflection_angle.nominal_value)
-        self.assertEqual(45.0, actual_deflection_angle.std_dev)
-
-    def test_clock_angle_calibration_table_from_file(self):
-        file_path = Path(
-            imap_l3_processing.__file__).parent.parent / 'tests' / 'test_data' / 'swapi' / 'imap_swapi_clock-angle-and-flow-deflection-lut_20240918_v000.dat'
-
-        proton_solar_wind_speed = 405.00
-        a_over_b = 0.01625
-
-        calibration_table = ClockAngleCalibrationTable.from_file(file_path)
-
-        self.assertAlmostEqual(6.105, calibration_table.lookup_clock_offset(proton_solar_wind_speed, a_over_b))
-        self.assertEqual(1.625, calibration_table.lookup_flow_deflection(proton_solar_wind_speed, a_over_b))
diff --git a/tests/swapi/l3a/science/test_calculate_proton_solar_wind_speed.py b/tests/swapi/l3a/science/test_calculate_proton_solar_wind_speed.py
deleted file mode 100644
index 5f4c4ea0f..000000000
--- a/tests/swapi/l3a/science/test_calculate_proton_solar_wind_speed.py
+++ /dev/null
@@ -1,376 +0,0 @@
-import math
-from datetime import datetime
-from pathlib import Path
-from unittest.mock import patch
-
-import numpy as np
-import spiceypy
-from scipy.optimize import curve_fit
-from spacepy.pycdf import CDF
-from uncertainties import ufloat
-from uncertainties.unumpy import uarray, std_devs, nominal_values
-
-import imap_l3_processing
-from imap_l3_processing.constants import METERS_PER_KILOMETER, ONE_SECOND_IN_NANOSECONDS
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_speed import calculate_proton_solar_wind_speed, \
-    get_peak_indices, find_peak_center_of_mass_index, interpolate_energy, fit_energy_per_charge_peak_variations, \
-    calculate_sw_speed_h_plus, get_proton_peak_indices, interpolate_angle, get_angle, \
-    get_spin_angle_from_swapi_axis_in_despun_frame, estimate_deflection_and_clock_angles
-from tests.spice_test_case import SpiceTestCase
-
-
-class TestCalculateProtonSolarWindSpeed(SpiceTestCase):
-    def test_calculate_solar_wind_speed(self):
-        energies = np.array([[0, 1000, 750, 500, 400, 300, 200, 100, 50]] * 5)
-        count_rates = np.array([[0, 14, 15, 16, 17, 16, 15, 14, 0],
-                                [0, 14, 15, 16, 17, 16, 15, 14, 0],
-                                [0, 14, 15, 16, 17, 16, 15, 14, 0],
-                                [0, 14, 15, 16, 17, 16, 15, 14, 0],
-                                [0, 14, 15, 16, 17, 16, 15, 14, 0]])
-        count_rates_with_uncertainties = uarray(count_rates, np.full_like(count_rates, 1.0))
-
-        times = [datetime(2025, 6, 6, 12, i) for i in range(5)]
-        epochs_in_terrestrial_time = spiceypy.datetime2et(times) * ONE_SECOND_IN_NANOSECONDS
-        speed, a, phi, b, chi_sq = calculate_proton_solar_wind_speed(count_rates_with_uncertainties, energies,
-                                                             epochs_in_terrestrial_time)
-
-        proton_charge = 1.602176634e-19
-        proton_mass = 1.67262192595e-27
-        expected_speed = math.sqrt(2 * 400 * proton_charge / proton_mass) / METERS_PER_KILOMETER
-        self.assertAlmostEqual(expected_speed, speed.n, 0)
-        self.assertAlmostEqual(0, chi_sq)
-
-    def test_calculate_solar_wind_speed_throws_exception_with_too_few_count_rates(self):
-        energies = np.array([[0, 1000, 750, 500, 400, 300, 200, 100, 50]] * 5)
-        count_rates = np.array([[0, 14, 15, 16, 17, 16, 15, 14, 0],
-                                [0, 14, 15, 16, 17, 16, 15, 14, 0],
-                                [0, 14, 15, 16, 17, 16, 15, 14, 0],
-                                [0, 14, 14, 14, 0, 0, 0, 0, 0],
-                                [0, 14, 14, 14, 0, 0, 0, 0, 0]])
-        count_rates_with_uncertainties = uarray(count_rates, np.full_like(count_rates, 1.0))
-
-        times = [datetime(2025, 6, 6, 12, i) for i in range(5)]
-        epochs_in_terrestrial_time = spiceypy.datetime2et(times) * ONE_SECOND_IN_NANOSECONDS
-        with self.assertRaises(Exception):
-            calculate_proton_solar_wind_speed(count_rates_with_uncertainties, energies,
-                                              epochs_in_terrestrial_time)
-
-    def test_calculate_solar_wind_speed_from_model_data(self):
-        file_path = Path(
-            imap_l3_processing.__file__).parent.parent / 'tests' / 'test_data' / 'swapi' / 'imap_swapi_l2_fake-menlo-5-sweeps_20250606_v001.cdf'
-        with CDF(str(file_path)) as cdf:
-            epoch = cdf.raw_var("epoch")[...]
-            energy = cdf["esa_energy"][...]
-            count_rate = cdf["swp_coin_rate"][...]
-            count_rate_delta = cdf["swp_coin_unc"][...]
-        speed, a, phi, b, chi_sq = calculate_proton_solar_wind_speed(uarray(count_rate, count_rate_delta), energy,
-                                                             epoch)
-
-        self.assertAlmostEqual(speed.n, 497.930, 3)
-        self.assertAlmostEqual(speed.s, 0.4615, 3)
-        self.assertAlmostEqual(a.n, 32.033, 3)
-        self.assertAlmostEqual(a.s, 3.4696, 2)
-        self.assertAlmostEqual(phi.n, 10.6, 1)
-        self.assertAlmostEqual(phi.s, 5.862, 2)
-        self.assertAlmostEqual(b.n, 1294, 0)
-        self.assertAlmostEqual(b.s, 2.4, 1)
-        self.assertAlmostEqual(chi_sq, 0.473, 1)
-
-    def test_get_peak_indices(self):
-        test_cases = [
-            ("one clear peak", [0, 0, 5, 10, 5, 0, 0], 2, [0, 5, 10, 5, 0]),
-            ("narrow width", [0, 0, 5, 10, 5, 0, 0], 1, [5, 10, 5]),
-            ("wide peak", [0, 0, 5, 10, 10, 2, 0, 0], 2, [0, 5, 10, 10, 2, 0]),
-            ("At left edge", [5, 10, 5, 0, 0], 2, [5, 10, 5, 0]),
-            ("At right edge", [0, 0, 5, 10, ], 2, [0, 5, 10]),
-        ]
-
-        for name, count_rates, width, expected_peak_values in test_cases:
-            with self.subTest(name):
-                peak_indices = get_peak_indices(count_rates, width)
-                extracted_peak = count_rates[peak_indices]
-                self.assertEqual(expected_peak_values, extracted_peak)
-
-    def test_get_proton_peak_indices(self):
-        test_cases = [
-            ("one clear peak", [0, 0, 0, 1, 0, 5, 10, 5, 0, 0, 1, 1, 1], [0, 1, 0, 5, 10, 5, 0, 0, 1]),
-            ("tied for peak", [0, 0, 0, 1, 0, 5, 10, 10, 0, 0, 1, 1, 1], [0, 1, 0, 5, 10, 10, 0, 0, 1, 1]),
-        ]
-
-        for name, count_rates, expected_peak_values in test_cases:
-            with self.subTest(name):
-                peak_indices = get_proton_peak_indices(count_rates)
-                extracted_peak = count_rates[peak_indices]
-                self.assertEqual(expected_peak_values, extracted_peak)
-
-    def test_an_exception_is_raised_when_count_rates_contains_multiple_peaks(self):
-        test_cases = [
-            ("two clear peak", [0, 0, 5, 10, 5, 0, 0, 2, 10, 2, 0, 0]),
-            ("peak is too wide", [0, 0, 5, 10, 10, 10, 2, 0, 0]),
-        ]
-
-        for name, count_rates in test_cases:
-            with self.subTest(name):
-                with self.assertRaises(Exception) as cm:
-                    get_peak_indices(count_rates, 2)
-                self.assertEqual(str(cm.exception), "Count rates contains multiple distinct peaks")
-
-    def test_find_peak_center_of_mass_index(self):
-        test_cases = [
-            ("symmetrical peak", [0, 0, 5, 10, 5, 0, 0], slice(2, 5), 0, 3),
-            ("asymmetrical peak", [0, 0, 5, 15, 0, 0, 0], slice(2, 5), 0, 2.75),
-            ("excluding low rates", [0, 2, 5, 10, 5, 2, 1], slice(0, 7), 2, 3),
-        ]
-        for name, count_rates, peak_slice, minimum_count_rate_inclusive, expected_peak_index in test_cases:
-            with self.subTest(name):
-                self.assertEqual(expected_peak_index,
-                                 find_peak_center_of_mass_index(peak_slice, count_rates, minimum_count_rate_inclusive))
-
-    def test_find_peak_center_of_mass_index_filters_correctly_with_uncertain_count_rates(self):
-        test_cases = [
-            ("uses nominal values to filter", uarray([0, 2, 2, 2, 6, 0, 0], [1, 1, 1, 1, 1, 1, 1]), slice(0, 7), 2, 3),
-        ]
-        for name, count_rates, peak_slice, minimum_count_rate_inclusive, expected_peak_index in test_cases:
-            with self.subTest(name):
-                self.assertEqual(expected_peak_index,
-                                 find_peak_center_of_mass_index(peak_slice, count_rates,
-                                                                minimum_count_rate_inclusive).nominal_value)
-
-    def test_uncertainty_calculation_in_find_peak_center_of_mass_index(self):
-        test_cases = [
-            ("with uncertainties", uarray([0, 0, 3, 5, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0]), slice(2, 5), 0, 2.625),
-            ("with sqrt uncertainties", uarray([0, 0, 6, 600, 6, 0, 0], [0, 0, 6, 60, 6, 0, 0]), slice(2, 5), 0, 3),
-        ]
-        for name, count_rates, peak_slice, minimum_count_rate_inclusive, expected_peak_index in test_cases:
-            with self.subTest(name):
-                count_rate_std_devs = std_devs(count_rates)
-
-                nominal_count_rates = nominal_values(count_rates)
-                base_result = find_peak_center_of_mass_index(peak_slice, nominal_count_rates,
-                                                             minimum_count_rate_inclusive)
-                deviations_for_single_inputs = []
-                for i in range(len(count_rate_std_devs)):
-                    DELTA = 1e-8
-                    modified_count_rates = nominal_count_rates.copy()
-                    modified_count_rates[i] += DELTA
-                    partial_derivative = (
-                                                 find_peak_center_of_mass_index(peak_slice, modified_count_rates,
-                                                                                minimum_count_rate_inclusive)
-                                                 - base_result) / DELTA
-                    deviations_for_single_inputs.append(count_rate_std_devs[i] * partial_derivative)
-                final_uncertainty = math.sqrt(sum(n * n for n in deviations_for_single_inputs))
-                result_with_uncertainty = find_peak_center_of_mass_index(peak_slice, count_rates,
-                                                                         minimum_count_rate_inclusive)
-
-                self.assertAlmostEqual(final_uncertainty, result_with_uncertainty.std_dev, 5)
-                self.assertEqual(base_result, result_with_uncertainty.n)
-
-    def test_find_peak_center_of_mass_index_raises_error_if_too_few_bins_remain(self):
-        test_cases = [
-            ("raises error", [0, 0, 5, 10, 1, 0, 0], slice(2, 5), 2, 3, True),
-            ("no error", [0, 0, 5, 15, 1, 0, 0], slice(2, 5), 2, 2, False),
-        ]
-        for name, count_rates, peak_slice, minimum_count_rate_inclusive, minimum_bin_count, expect_error in test_cases:
-            with self.subTest(name):
-                if expect_error:
-                    with self.assertRaises(Exception) as cm:
-                        find_peak_center_of_mass_index(peak_slice, count_rates, minimum_count_rate_inclusive,
-                                                       minimum_bin_count)
-                else:
-                    find_peak_center_of_mass_index(peak_slice, count_rates, minimum_count_rate_inclusive,
-                                                   minimum_bin_count)
-
-    def test_interpolates_to_find_energy_at_center_of_mass(self):
-        test_cases = [
-            ("Fractional Center of mass index", 1.5, [10, 100, 10000, 100000], 1000),
-            ("Fractional Center of mass index 2", 2 / 3, [2, 16, 1024], 8),
-            ("Whole number Center of mass index", 1, [3, 9, 27], 9),
-        ]
-
-        for name, center_of_mass_index, energies, expected_interpolated_energy in test_cases:
-            with self.subTest(name):
-                self.assertAlmostEqual(expected_interpolated_energy, interpolate_energy(center_of_mass_index, energies))
-
-    def test_interpolates_to_find_angle_at_center_of_mass(self):
-        test_cases = [
-            ("interpolates", [110, 230, 350, 110], 1.5, 290),
-            ("interpolates correctly across period", [110, 230, 350, 110], 2.5, 50)
-        ]
-
-        for name, angles, center_of_mass_index, expected_interpolated_angle in test_cases:
-            with self.subTest(name):
-                angle = interpolate_angle(center_of_mass_index, angles)
-
-                self.assertAlmostEqual(angle, expected_interpolated_angle)
-
-    def test_interpolates_with_uncertainty_to_find_energy_at_center_of_mass(self):
-        test_cases = [
-            ("Fractional Center of mass index", ufloat(1.5, 0.5), [10, 100, 10000, 100000], 1000),
-        ]
-
-        for name, center_of_mass_index, energies, expected_interpolated_energy in test_cases:
-            with self.subTest(name):
-                DELTA = 1e-8
-                deriv = (interpolate_energy(1.5 + DELTA, energies) - interpolate_energy(1.5, energies)) / DELTA
-
-                expected_uncertainty = center_of_mass_index.std_dev * deriv
-
-                result = interpolate_energy(center_of_mass_index, energies)
-                self.assertAlmostEqual(expected_interpolated_energy, result.n)
-                self.assertAlmostEqual(expected_uncertainty, result.std_dev, 3)
-
-    @patch('imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_speed.scipy.optimize.curve_fit')
-    def test_curve_fit_is_initialized_correctly(self, mock_curve_fit):
-        test_cases = [
-            ([30, 60, 90, 120], uarray([1319, 1328, 1329, 1323], 1), 5, 180, 1324.75),
-            ([30, 60, 90, 120], uarray([975, 956, 950, 957], 1), 12.5, 120, 959.5),
-        ]
-
-        mock_curve_fit.side_effect = curve_fit
-
-        for angles, energies, expected_initial_a, expected_initial_phi, expected_initial_b in test_cases:
-            with self.subTest():
-                (a, phi, b), chi_sq = fit_energy_per_charge_peak_variations(energies, angles)
-
-                curve_fit_parameters = mock_curve_fit.call_args.kwargs
-
-                self.assertEqual(expected_initial_a, curve_fit_parameters["p0"][0])
-                self.assertEqual(expected_initial_phi, curve_fit_parameters["p0"][1])
-                self.assertEqual(expected_initial_b, curve_fit_parameters["p0"][2])
-
-                a_lower_bound, phi_lower_bound, b_lower_bound = curve_fit_parameters["bounds"][0]
-                self.assertEqual(0, a_lower_bound)
-                self.assertEqual(-np.inf, phi_lower_bound)
-                self.assertEqual(0, b_lower_bound)
-
-                a_upper_bound, phi_upper_bound, b_upper_bound = curve_fit_parameters["bounds"][1]
-                self.assertEqual(np.inf, a_upper_bound)
-                self.assertEqual(np.inf, phi_upper_bound)
-                self.assertEqual(np.inf, b_upper_bound)
-
-    def test_returns_when_reduced_chi_squared_greater_than_10(self):
-        test_cases = [
-            ("less than 10 chi", [30, 60, 90, 120], uarray([1319, 1103, 1110, 1323], 1), True, 13.512723754922165),
-            ("greater than 10 chi", [30, 60, 90, 120], uarray([975, 956, 950, 971], 1), False, 9.071796769724411),
-        ]
-
-        for name, angles, energies, error_flag, expected_chi_squared in test_cases:
-            with self.subTest(name):
-                (a, phi, b), chi_squared = fit_energy_per_charge_peak_variations(energies, angles)
-                self.assertAlmostEqual(expected_chi_squared, chi_squared)
-
-    def test_curve_fit(self):
-        test_cases = [
-            (10, 500, 25, 0, 120),
-            (30, 1000, 270, 0, 2)
-        ]
-
-        for a, b, phi, expected_chi_sq, initial_angle in test_cases:
-            with self.subTest():
-                angles = [initial_angle - 72 * i for i in range(5)]
-                centers_of_mass = [b + a * np.sin(np.deg2rad(phi - angle)) for angle in angles]
-                center_of_mass_uncertainties = np.full_like(centers_of_mass, 1e-6)
-                centers_of_mass = uarray(centers_of_mass, center_of_mass_uncertainties)
-                (result_a, result_phi, result_b), chi_sq = fit_energy_per_charge_peak_variations(centers_of_mass,
-                                                                                                 angles)
-
-                self.assertAlmostEqual(a, result_a.nominal_value)
-                self.assertAlmostEqual(phi, result_phi.nominal_value)
-                self.assertAlmostEqual(b, result_b.nominal_value)
-                self.assertAlmostEqual(expected_chi_sq, chi_sq)
-
-    def test_converts_proton_energies_to_speeds(self):
-        test_cases = [
-            (1000, 437.6947142244463),
-            (800, 391.48605375928245),
-            (2000, 618.993801035228),
-        ]
-
-        for ev_q, expected_speed_km_s in test_cases:
-            with self.subTest(ev_q):
-                self.assertAlmostEqual(expected_speed_km_s, calculate_sw_speed_h_plus(ev_q))
-
-    def test_converts_proton_energies_with_uncertainties(self):
-        test_cases = [
-            (ufloat(1000, 10), ufloat(437.6947142244463, 2.1884735711222315)),
-            (ufloat(800, 100), ufloat(391.48605375928245, 24.46787835995515)),
-            (ufloat(2000, 2), ufloat(618.993801035228, 0.30949690051761405)),
-        ]
-
-        for ev_q, expected_speed_km_s in test_cases:
-            with self.subTest(ev_q):
-                sw_speed = calculate_sw_speed_h_plus(ev_q)
-                self.assertAlmostEqual(expected_speed_km_s.n, sw_speed.n)
-                self.assertAlmostEqual(expected_speed_km_s.s, sw_speed.s)
-
-    def test_calculate_sw_speed_on_array_with_uncertainties(self):
-        energy = np.array([
-            ufloat(1000.0, 10.0),
-            ufloat(800.0, 100.0),
-            ufloat(2000.0, 2.0)
-        ], dtype=object)
-        expected = [ufloat(437.6947142244463, 2.1884735711222315), ufloat(391.48605375928245, 24.46787835995515),
-                    ufloat(618.993801035228, 0.30949690051761405)]
-        np.testing.assert_array_equal(nominal_values(expected), nominal_values(calculate_sw_speed_h_plus(energy)))
-        np.testing.assert_array_almost_equal(std_devs(expected), std_devs(calculate_sw_speed_h_plus(energy)))
-
-    def test_calculate_sw_speed_on_array_without_uncertainties(self):
-        energy = np.array([1000.0, 800.0, 2000.0], dtype=float)
-        expected = [437.6947142244463, 391.48605375928245, 618.993801035228]
-        result = calculate_sw_speed_h_plus(energy)
-        self.assertIsInstance(result, np.ndarray)
-        self.assertEqual(float, result.dtype)
-        np.sqrt(result)
-        np.testing.assert_array_equal(expected, result)
-
-    def test_calculate_sw_speed_on_2d_array(self):
-        energy = uarray([[1000]], [[10]])
-        expected = uarray([[437.6947142244463]], [[2.1884735711222315]])
-        result = calculate_sw_speed_h_plus(energy)
-        np.testing.assert_array_almost_equal(nominal_values(expected), nominal_values(result))
-        np.testing.assert_array_almost_equal(std_devs(expected), std_devs(result))
-
-    def test_calculate_sw_speed_on_empty_array(self):
-        energy = []
-        expected = []
-        result = calculate_sw_speed_h_plus(energy)
-        np.testing.assert_array_equal(expected, result)
-
-    @patch('imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_speed.spiceypy.pxform')
-    @patch('imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_speed.spiceypy.unitim')
-    def test_get_angle(self, mock_unitim, mock_pxform):
-        mock_pxform.return_value = [[1, 0, 0], [0, 0, -1], [0, -1, 0]]
-
-        epoch = 123
-
-        actual_angle = get_angle(epoch)
-
-        mock_unitim.assert_called_with((epoch / ONE_SECOND_IN_NANOSECONDS), "TT", "ET")
-        mock_pxform.assert_called_with("IMAP_SWAPI", "IMAP_DPS", mock_unitim.return_value)
-
-        self.assertEqual(0, actual_angle)
-
-    def test_get_spin_angle_from_swapi_axis_in_despun_frame(self):
-        cases = [
-            ([1, 0, 0], 270),
-            ([0, 1, 0], 0),
-            ([0, -1, 0], 180),
-            ([-1, 0, 0], 90),
-            ([-1, 0, 50], 90),
-        ]
-        for swapi_axis, expected_angle in cases:
-            with self.subTest(swapi_axis):
-                self.assertAlmostEqual(expected_angle,
-                                       get_spin_angle_from_swapi_axis_in_despun_frame(np.array(swapi_axis)))
-
-    def test_estimate_deflection_and_clock_angles(self):
-        cases = [
-            (29.78, 86, 270),
-            (400, 0.2696213, 270),
-            (800, 1.8666717, 90),
-        ]
-        for speed, expected_deflection, expected_clock in cases:
-            with self.subTest(speed):
-                deflection, clock = estimate_deflection_and_clock_angles(speed)
-                self.assertAlmostEqual(expected_deflection, deflection)
-                self.assertAlmostEqual(expected_clock, clock)
diff --git a/tests/swapi/l3a/science/test_calculate_proton_solar_wind_temperature_and_density.py b/tests/swapi/l3a/science/test_calculate_proton_solar_wind_temperature_and_density.py
deleted file mode 100644
index b3a545f08..000000000
--- a/tests/swapi/l3a/science/test_calculate_proton_solar_wind_temperature_and_density.py
+++ /dev/null
@@ -1,330 +0,0 @@
-import itertools
-from dataclasses import astuple
-from pathlib import Path
-from unittest import TestCase
-
-import numpy as np
-from spacepy.pycdf import CDF
-from uncertainties import ufloat
-from uncertainties.unumpy import uarray, std_devs
-
-import imap_l3_processing
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_temperature_and_density import \
-    proton_count_rate_model, \
-    calculate_proton_solar_wind_temperature_and_density_for_one_sweep, ProtonTemperatureAndDensityCalibrationTable, \
-    calculate_uncalibrated_proton_solar_wind_temperature_and_density, \
-    calculate_proton_solar_wind_temperature_and_density
-from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
-from tests.swapi.l3a.science.test_calculate_alpha_solar_wind_speed import synthesize_uncertainties
-from tests.test_helpers import get_test_data_path
-
-
-class TestCalculateProtonSolarWindTemperatureAndDensity(TestCase):
-    def test_uncalibrated_calculate_a_single_sweep_from_example_file(self):
-        file_path = Path(
-            imap_l3_processing.__file__).parent.parent / 'tests' / 'test_data' / 'swapi' / 'imap_swapi_l2_fake-menlo-5-sweeps_20100101_v002.cdf'
-        with CDF(str(file_path)) as cdf:
-            energy = cdf["esa_energy"][...]
-            count_rate = cdf["swp_coin_rate"][...]
-            count_rate_delta = cdf["swp_coin_unc"][...]
-
-        efficiency = 0.1
-
-        proton_solar_wind_temp_and_density = calculate_proton_solar_wind_temperature_and_density_for_one_sweep(
-            uarray(count_rate, count_rate_delta)[4], energy[4], efficiency)
-
-        self.assertAlmostEqual(100349.82969792404, proton_solar_wind_temp_and_density.temperature.nominal_value, 0)
-        self.assertAlmostEqual(6277.912502, proton_solar_wind_temp_and_density.temperature.std_dev, 0)
-        self.assertAlmostEqual(0.0875345 / efficiency, proton_solar_wind_temp_and_density.density.nominal_value, 2)
-        self.assertAlmostEqual(0.0036216 / efficiency, proton_solar_wind_temp_and_density.density.std_dev, 3)
-        self.assertEqual(SwapiL3Flags.NONE, proton_solar_wind_temp_and_density.bad_fit_flag)
-
-    def test_uncalibrated_calculate_using_five_sweeps_from_example_file(self):
-        file_path = Path(
-            imap_l3_processing.__file__).parent.parent / 'tests' / 'test_data' / 'swapi' / 'imap_swapi_l2_fake-menlo-5-sweeps_20100101_v002.cdf'
-        with CDF(str(file_path)) as cdf:
-            energy = cdf["esa_energy"][...]
-            count_rate = cdf["swp_coin_rate"][...]
-            count_rate_delta = cdf["swp_coin_unc"][...]
-
-        efficiency = 0.8
-        actual_proton_temp_density = calculate_uncalibrated_proton_solar_wind_temperature_and_density(
-            uarray(count_rate, count_rate_delta), energy, efficiency)
-
-        np.testing.assert_allclose(98674.370199, actual_proton_temp_density.temperature.nominal_value, rtol=1e-5)
-        np.testing.assert_allclose(2348.265738, actual_proton_temp_density.temperature.std_dev, rtol=1e-3)
-        np.testing.assert_allclose(0.11530 / efficiency, actual_proton_temp_density.density.nominal_value, rtol=1e-4)
-        np.testing.assert_allclose(0.0018697 / efficiency, actual_proton_temp_density.density.std_dev, rtol=1e-4)
-
-    def test_averages_successful_sweeps_if_at_least_three(self):
-        file_path = Path(
-            imap_l3_processing.__file__).parent.parent / 'tests' / 'test_data' / 'swapi' / 'imap_swapi_l2_fake-menlo-5-sweeps_20100101_v002.cdf'
-        with CDF(str(file_path)) as cdf:
-            energy = cdf["esa_energy"][...]
-            count_rate = cdf["swp_coin_rate"][...]
-            count_rate_delta = cdf["swp_coin_unc"][...]
-
-        count_rate_delta[1, :] = 1e-6
-        count_rate_delta[2, :] = 1e-6
-
-        efficiency = 0.8
-
-        valid_temp_density_1 = calculate_proton_solar_wind_temperature_and_density_for_one_sweep(
-            uarray(count_rate, count_rate_delta)[0, :], energy[0, :],
-            efficiency)
-        valid_temp_density_2 = calculate_proton_solar_wind_temperature_and_density_for_one_sweep(
-            uarray(count_rate, count_rate_delta)[3, :], energy[3, :],
-            efficiency)
-        valid_temp_density_3 = calculate_proton_solar_wind_temperature_and_density_for_one_sweep(
-            uarray(count_rate, count_rate_delta)[4, :], energy[4, :],
-            efficiency)
-
-        temps = [valid_temp_density_1.temperature, valid_temp_density_2.temperature, valid_temp_density_3.temperature]
-        densities = [valid_temp_density_1.density, valid_temp_density_2.density, valid_temp_density_3.density]
-        expected_average_temp = np.average(temps, weights=1 / std_devs(temps) ** 2)
-        expected_average_density = np.average(densities, weights=1 / std_devs(densities) ** 2)
-
-        actual_proton_temp_density = calculate_uncalibrated_proton_solar_wind_temperature_and_density(
-            uarray(count_rate, count_rate_delta), energy, efficiency)
-
-        np.testing.assert_allclose(actual_proton_temp_density.temperature.nominal_value,
-                                   expected_average_temp.nominal_value, rtol=1e-5)
-        np.testing.assert_allclose(actual_proton_temp_density.temperature.std_dev, expected_average_temp.std_dev,
-                                   rtol=1e-3)
-        np.testing.assert_allclose(actual_proton_temp_density.density.nominal_value,
-                                   expected_average_density.nominal_value, rtol=1e-4)
-        np.testing.assert_allclose(actual_proton_temp_density.density.std_dev, expected_average_density.std_dev,
-                                   rtol=1e-4)
-        self.assertEqual(SwapiL3Flags.NONE, actual_proton_temp_density.bad_fit_flag)
-
-    def test_raises_error_when_fewer_than_three_sweeps_fit_successfully(self):
-        file_path = Path(
-            imap_l3_processing.__file__).parent.parent / 'tests' / 'test_data' / 'swapi' / 'imap_swapi_l2_fake-menlo-5-sweeps_20100101_v002.cdf'
-        with CDF(str(file_path)) as cdf:
-            energy = cdf["esa_energy"][...]
-            count_rate = cdf["swp_coin_rate"][...]
-            count_rate_delta = cdf["swp_coin_unc"][...]
-
-        count_rate_delta[0, :] = 1e-6
-        count_rate_delta[1, :] = 1e-6
-        count_rate_delta[2, :] = 1e-6
-
-        efficiency = 0.8
-
-        invalid_temp_density_0 = calculate_proton_solar_wind_temperature_and_density_for_one_sweep(
-            uarray(count_rate, count_rate_delta)[0, :], energy[0, :],
-            efficiency)
-        invalid_temp_density_1 = calculate_proton_solar_wind_temperature_and_density_for_one_sweep(
-            uarray(count_rate, count_rate_delta)[1, :], energy[1, :],
-            efficiency)
-        invalid_temp_density_2 = calculate_proton_solar_wind_temperature_and_density_for_one_sweep(
-            uarray(count_rate, count_rate_delta)[2, :], energy[2, :],
-            efficiency)
-        valid_temp_density_3 = calculate_proton_solar_wind_temperature_and_density_for_one_sweep(
-            uarray(count_rate, count_rate_delta)[3, :], energy[3, :],
-            efficiency)
-        valid_temp_density_4 = calculate_proton_solar_wind_temperature_and_density_for_one_sweep(
-            uarray(count_rate, count_rate_delta)[4, :], energy[4, :],
-            efficiency)
-
-        temps = [invalid_temp_density_0.temperature, invalid_temp_density_1.temperature,
-                 invalid_temp_density_2.temperature, valid_temp_density_3.temperature, valid_temp_density_4.temperature]
-        densities = [invalid_temp_density_0.density, invalid_temp_density_1.density, invalid_temp_density_2.density,
-                     valid_temp_density_3.density, valid_temp_density_4.density]
-
-        expected_average_temp = np.average(temps, weights=1 / std_devs(temps) ** 2)
-        expected_average_density = np.average(densities, weights=1 / std_devs(densities) ** 2)
-
-        actual_proton_temp_density = calculate_uncalibrated_proton_solar_wind_temperature_and_density(
-            uarray(count_rate, count_rate_delta), energy, efficiency)
-
-        np.testing.assert_allclose(actual_proton_temp_density.temperature.nominal_value,
-                                   expected_average_temp.nominal_value, rtol=1e-5)
-        np.testing.assert_allclose(actual_proton_temp_density.temperature.std_dev, expected_average_temp.std_dev,
-                                   rtol=1e-3)
-        np.testing.assert_allclose(actual_proton_temp_density.density.nominal_value,
-                                   expected_average_density.nominal_value, rtol=1e-4)
-        np.testing.assert_allclose(actual_proton_temp_density.density.std_dev, expected_average_density.std_dev,
-                                   rtol=1e-4)
-        self.assertEqual(SwapiL3Flags.HI_CHI_SQ, actual_proton_temp_density.bad_fit_flag)
-
-    def test_calibrate_density_and_temperature_using_lookup_table(self):
-        speed_values = [250, 1000]
-        deflection_angle_values = [0, 6]
-        clock_angle_values = [0, 360]
-        density_values = [1, 10]
-        temperature_values = [1000, 100000]
-
-        lookup_table = self.generate_lookup_table(speed_values, deflection_angle_values, clock_angle_values,
-                                                  density_values, temperature_values)
-
-        temperature = lookup_table.calibrate_temperature(ufloat(450, 2), ufloat(3, 0.1),
-                                                         ufloat(1, 1), ufloat(4, 0.1),
-                                                         ufloat(50000, 10000))
-        density = lookup_table.calibrate_density(ufloat(450, 2), ufloat(3, 0.1), ufloat(1, 1),
-                                                 ufloat(4, 0.1), ufloat(50000, 10000))
-
-        self.assertAlmostEqual(50000 * 0.97561, temperature.n, 3)
-        self.assertAlmostEqual(10000 * 0.97561, temperature.s, 3)
-
-        self.assertAlmostEqual(4 * 1.021, density.n)
-        self.assertAlmostEqual(0.1 * 1.021, density.s)
-
-    def test_lookup_table_handles_data_that_does_not_extend_to_360_degrees(self):
-        speed_values = [250, 1000]
-        deflection_angle_values = [0, 6]
-        clock_angle_values = [0, 90, 180, 270]
-        density_values = [1, 10]
-        temperature_values = [1000, 100000]
-
-        lookup_table = self.generate_lookup_table(speed_values, deflection_angle_values, clock_angle_values,
-                                                  density_values, temperature_values)
-
-        temperature = lookup_table.calibrate_temperature(ufloat(450, 2), ufloat(3, 0.1),
-                                                         ufloat(350, 1), ufloat(4, 0.1),
-                                                         ufloat(50000, 10000))
-        density = lookup_table.calibrate_density(ufloat(450, 2), ufloat(3, 0.1), ufloat(350, 1),
-                                                 ufloat(4, 0.1), ufloat(50000, 10000))
-
-        self.assertAlmostEqual(50000 * 0.97561, temperature.n, 3)
-        self.assertAlmostEqual(10000 * 0.97561, temperature.s, 3)
-
-        self.assertAlmostEqual(4 * 1.021, density.n)
-        self.assertAlmostEqual(0.1 * 1.021, density.s)
-
-    def test_density_temperature_lookup_table_works_with_clock_angles_outside_0_to_360(self):
-        speed_values = [250, 1000]
-        deflection_angle_values = [0, 6]
-        clock_angle_values = [0, 360]
-        density_values = [1, 10]
-        temperature_values = [1000, 100000]
-
-        lookup_table = self.generate_lookup_table(speed_values, deflection_angle_values, clock_angle_values,
-                                                  density_values, temperature_values)
-
-        temperature = lookup_table.calibrate_temperature(ufloat(450, 2), ufloat(3, 0.1),
-                                                         ufloat(0.0, 1), ufloat(4, 0.1),
-                                                         ufloat(50000, 10000))
-
-        density = lookup_table.calibrate_density(ufloat(450, 2), ufloat(3, 0.1), ufloat(360, 1),
-                                                 ufloat(4, 0.1), ufloat(50000, 10000))
-
-        self.assertAlmostEqual(0.97561 * 50000, temperature.n, places=3)
-        self.assertAlmostEqual(0.97561 * 10000, temperature.s, places=3)
-
-        self.assertAlmostEqual(1.021 * 4, density.n)
-        self.assertAlmostEqual(1.021 * 0.1, density.s)
-
-    def test_calculate_temperature_and_density(self):
-        speed_values = [250, 1000]
-        deflection_angle_values = [0, 6]
-        clock_angle_values = [0, 360]
-        density_values = [1, 10]
-        temperature_values = [1000, 200000]
-        efficiency = 0.1
-
-        lookup_table = self.generate_lookup_table(speed_values, deflection_angle_values, clock_angle_values,
-                                                  density_values, temperature_values)
-
-        file_path = get_test_data_path('swapi/imap_swapi_l2_fake-menlo-5-sweeps_20100101_v002.cdf')
-        with CDF(str(file_path)) as cdf:
-            energy = cdf["esa_energy"][...]
-            count_rate = cdf["swp_coin_rate"][...]
-            count_rate_delta = cdf["swp_coin_unc"][...]
-
-        temperature, density, bad_fit_flag = astuple(
-            calculate_proton_solar_wind_temperature_and_density(lookup_table, ufloat(450, 2),
-                                                                ufloat(3, 0.1), ufloat(1, 1),
-                                                                uarray(count_rate, count_rate_delta),
-                                                                energy, efficiency))
-
-        np.testing.assert_allclose(98674.370199 * 0.97561, temperature.nominal_value, rtol=1e-4)
-        np.testing.assert_allclose(2348.265738 * 0.97561, temperature.std_dev, rtol=2e-4)
-
-        np.testing.assert_allclose(0.11530 * 1.021 / efficiency, density.nominal_value, rtol=1e-4)
-        np.testing.assert_allclose(0.0018697 * 1.021 / efficiency, density.std_dev, rtol=1e-4)
-
-        self.assertEqual(SwapiL3Flags.NONE, bad_fit_flag)
-
-    def test_proton_count_rate_model_accounts_for_efficiency(self):
-        efficiency_factor = 0.5
-
-        cases = [
-            (np.array([800, 900, 1000]), 1, 2, 1e6, 700, np.array([9.406743, 25.85924, 62.665463])),
-            (np.array([800, 900, 1000]), efficiency_factor, 2, 1e6, 700,
-             np.array([9.406743, 25.85924, 62.665463]) * efficiency_factor),
-        ]
-
-        for ev_per_q, efficiency, density_per_cm3, temperature, bulk_flow_speed_km_per_s, expected in cases:
-            result = proton_count_rate_model(efficiency, ev_per_q, density_per_cm3, temperature,
-                                             bulk_flow_speed_km_per_s)
-            np.testing.assert_array_almost_equal(result, expected)
-
-    def test_can_recover_density_and_temperature_from_model_data(self):
-        test_cases = [
-            (5, 100e3, 450),
-            (3, 100e3, 450),
-            (3, 80e3, 550),
-            (3, 80e3, 750),
-            (3, 200e3, 750),
-            (0.5, 1.2e5, 600),
-            (30, 200e3, 750),
-        ]
-        energy = np.geomspace(100, 19000, 62)
-        efficiency = 0.02348
-        for density, temperature, speed in test_cases:
-            with self.subTest(f"{density}cm^-3, {temperature}K, {speed}km/s"):
-                count_rates = proton_count_rate_model(efficiency, energy, density, temperature, speed)
-                fake_uncertainties = synthesize_uncertainties(count_rates)
-                count_rates_with_uncertainties = uarray(count_rates, fake_uncertainties)
-                fit_temperature, fit_density, _ = astuple(
-                    calculate_proton_solar_wind_temperature_and_density_for_one_sweep(
-                        count_rates_with_uncertainties, energy, efficiency))
-                np.testing.assert_allclose(density, fit_density.nominal_value, rtol=3e-2)
-                np.testing.assert_allclose(temperature, fit_temperature.nominal_value, rtol=3e-2)
-
-    def test_throws_error_when_chi_squared_over_ten(self):
-        coincident_count_rates = uarray([100, 200, 300, 150, 50], 5)
-        energy = np.array([1000, 900, 800, 700, 600])
-        expected_flag = SwapiL3Flags.HI_CHI_SQ
-
-        proton_solar_wind_temp_and_density = calculate_proton_solar_wind_temperature_and_density_for_one_sweep(
-            coincident_count_rates, energy, 1)
-
-        self.assertAlmostEqual(77665.54856, proton_solar_wind_temp_and_density.temperature.nominal_value,
-                               3)
-        self.assertAlmostEqual(3676.14173, proton_solar_wind_temp_and_density.temperature.std_dev, 3)
-        self.assertAlmostEqual(0.03594, proton_solar_wind_temp_and_density.density.nominal_value,
-                               5)
-        self.assertAlmostEqual(0.0006567, proton_solar_wind_temp_and_density.density.std_dev, 7)
-        self.assertEqual(expected_flag, proton_solar_wind_temp_and_density.bad_fit_flag)
-
-    def test_filters_out_count_rates_below_13(self):
-        count_rates = uarray([100, 200, 300, 12, 50, 20], 30)
-        energy = np.array([1000, 900, 800, 700, 600, 500])
-
-        proton_temperature_density_1 = calculate_proton_solar_wind_temperature_and_density_for_one_sweep(count_rates,
-                                                                                                         energy, 1)
-
-        count_rates = uarray([100, 200, 300, 0, 50, 20], 30)
-        energy = np.array([1000, 900, 800, 700, 600, 500])
-
-        proton_temperature_density_2 = calculate_proton_solar_wind_temperature_and_density_for_one_sweep(count_rates,
-                                                                                                         energy, 1)
-
-        self.assertEqual(proton_temperature_density_1.temperature.nominal_value,
-                         proton_temperature_density_2.temperature.nominal_value)
-        self.assertEqual(proton_temperature_density_1.density.nominal_value,
-                         proton_temperature_density_2.density.nominal_value)
-
-    def generate_lookup_table(self, speed_values, deflection_angle_values, clock_angle_values, density_values,
-                              temperature_values):
-
-        coords = (speed_values, deflection_angle_values, clock_angle_values, density_values, temperature_values)
-
-        lut_rows = []
-        for (speed, deflection, clock_angle, density, temperature) in itertools.product(*coords):
-            output_density = 1.021 * density
-            output_temperature = 0.97561 * temperature
-            lut_rows.append([speed, deflection, clock_angle, density, output_density, temperature, output_temperature])
-        return ProtonTemperatureAndDensityCalibrationTable(np.array(lut_rows))
diff --git a/tests/swapi/l3a/test_chunk_fits.py b/tests/swapi/l3a/test_chunk_fits.py
new file mode 100644
index 000000000..a65339655
--- /dev/null
+++ b/tests/swapi/l3a/test_chunk_fits.py
@@ -0,0 +1,811 @@
+import unittest
+from datetime import datetime
+from unittest.mock import Mock, patch
+import platform
+
+import numpy as np
+from uncertainties import ufloat
+
+from imap_l3_processing.constants import (
+    ALPHA_MASS_PER_CHARGE_M_P_PER_E,
+    ALPHA_PARTICLE_MASS_KG,
+    ONE_SECOND_IN_NANOSECONDS,
+    PROTON_MASS_KG,
+    PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    THIRTY_SECONDS_IN_NANOSECONDS,
+)
+from imap_l3_processing.models import InputMetadata, MagData
+from imap_l3_processing.swapi.swapi_processor import SwapiProcessor
+from imap_l3_processing.swapi.l3a import chunk_fits
+from imap_l3_processing.swapi.l3a.chunk_fits import (
+    AlphaChunkFitter,
+    ChunkFitter,
+    ParallelChunkRunner,
+    ProtonChunkFitter,
+)
+from imap_l3_processing.swapi.l3b.science.efficiency_calibration_table import (
+    EfficiencyCalibrationTable,
+)
+from imap_l3_processing.swapi.l3a.models import SwapiL2Data
+from imap_l3_processing.swapi.l3a.science.solar_wind.fit_context import (
+    build_solar_wind_fit_context,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.forward_model import (
+    model_solar_wind_ideal_coincidence_rates,
+)
+from imap_l3_processing.swapi.l3a.science.solar_wind.params import SolarWindParams
+from imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_model import (
+    ProtonSolarWindFitResult,
+)
+from imap_l3_processing.swapi.l3a.utils import get_swapi_geometry
+from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
+from imap_l3_processing.swapi.response.deadtime import deadtime_factor
+from imap_l3_processing.swapi.constants import (
+    SWAPI_L2_K_FACTOR,
+    SWAPI_SCIENCE_BINS,
+)
+from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
+from tests.spice_test_case import SpiceTestCase
+from tests.swapi._helpers import REALISTIC_ESA_VOLTAGES
+from tests.test_helpers import get_test_instrument_team_data_path
+
+
+_N_SWEEPS = 5
+_N_BINS = 72
+_FULL_ENERGY = np.concatenate([[1.0e4], REALISTIC_ESA_VOLTAGES]) * SWAPI_L2_K_FACTOR
+
+_TRUE_DENSITY = 5.0
+_TRUE_TEMPERATURE_K = 1.0e5
+_TRUE_BULK_SPEED = 450.0
+# Sunward Parker spiral, off-nominal: 55° from -R toward +T (vs. nominal
+# 45° from +R toward -T), tilted 10° out of the ecliptic toward +N.
+_B_HAT_RTN = np.array([
+    -np.cos(np.radians(55.0)) * np.cos(np.radians(10.0)),
+    np.sin(np.radians(55.0)) * np.cos(np.radians(10.0)),
+    np.sin(np.radians(10.0)),
+])
+_TRUE_ALPHA_DENSITY = 0.2
+_TRUE_ALPHA_TEMPERATURE_K = 4.0e5
+_TRUE_DELTA_V_KM_S = 30.0
+_SC_VELOCITY_RTN = np.array([0.0, 30.0, 0.0])
+_EPOCH_TT2000 = 800_000_000_000_000_000
+_CHUNK_EPOCH = _EPOCH_TT2000 + THIRTY_SECONDS_IN_NANOSECONDS
+_SCI_START_TIME = _EPOCH_TT2000 + np.arange(_N_SWEEPS, dtype=np.int64) * 12_000_000_000
+
+# Every non-flag, non-epoch field must NaN-fill on short-circuit branches.
+_PROTON_SCALAR_KEYS = [
+    "proton_sw_speed", "proton_sw_speed_uncert",
+    "proton_sw_speed_sun", "proton_sw_speed_sun_uncert",
+    "proton_sw_temperature", "proton_sw_temperature_uncert",
+    "proton_sw_density", "proton_sw_density_uncert",
+]
+_PROTON_ARRAY_KEYS = [
+    "proton_sw_bulk_velocity_rtn_sun", "proton_sw_bulk_velocity_rtn_sun_covariance",
+    "proton_sw_bulk_velocity_rtn_sc", "proton_sw_bulk_velocity_rtn_sc_covariance",
+]
+_ALPHA_SCALAR_KEYS = [
+    "alpha_sw_density", "alpha_sw_density_uncert",
+    "alpha_sw_temperature", "alpha_sw_temperature_uncert",
+    "alpha_sw_delta_v", "alpha_sw_delta_v_uncert",
+]
+_ALPHA_ARRAY_KEYS = [
+    "alpha_sw_velocity_rtn", "alpha_sw_velocity_covariance_rtn",
+    "alpha_sw_b_hat_rtn",
+]
+_ALPHA_BUMP_BINS = slice(24, 31)
+
+
+def _swapi_response_with_warm_cache(voltages):
+    resp = SwapiResponse.from_files(
+        get_test_instrument_team_data_path("swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv"),
+        get_test_instrument_team_data_path("swapi/imap_swapi_central-effective-area_20260425_v001.csv"),
+        get_test_instrument_team_data_path("swapi/imap_swapi_passband-fit-coefficients_20260425_v001.csv"),
+    )
+    resp.warm_cache(voltages)
+    return resp
+
+
+def _spice_rotations(bin_slice):
+    """SPICE-derived SWAPI→RTN rotations at the synthetic chunk's measurement
+    times over `bin_slice`."""
+    bin_indices = np.arange(bin_slice.start, bin_slice.stop)
+    measurement_times = (
+        _SCI_START_TIME[:, np.newaxis]
+        + bin_indices * (12 / 72 * ONE_SECOND_IN_NANOSECONDS)
+    ).flatten()
+    return get_swapi_geometry(measurement_times)
+
+
+def _truth_velocity_rtn(rotations):
+    """Truth wind vector: `_TRUE_BULK_SPEED` km/s anti-parallel-ish to the SWAPI
+    boresight (column 1 of the SWAPI→RTN rotation at the chunk's first bin),
+    tilted 5° toward a stable in-plane direction. The deflection lifts the
+    wind off the spin axis so clock-angle assertions are non-degenerate."""
+    spin_axis_rtn = rotations[0, :, 1]
+    perpendicular = np.cross(spin_axis_rtn, [1.0, 0.0, 0.0])
+    perpendicular /= np.linalg.norm(perpendicular)
+    deflection = np.radians(5.0)
+    direction = -spin_axis_rtn * np.cos(deflection) + perpendicular * np.sin(deflection)
+    return _TRUE_BULK_SPEED * direction
+
+
+def _efficiency_table():
+    """Synthetic `EfficiencyCalibrationTable` with realistic in-flight proton
+    (0.12) and alpha (0.15) efficiencies and a lab-cal proton efficiency of 0.12."""
+    table = EfficiencyCalibrationTable.__new__(EfficiencyCalibrationTable)
+    table.data = np.array(
+        [
+            (np.datetime64("2024-01-01", "ns"), 0, 0.12, 0.15),
+            (np.datetime64("2025-11-01", "ns"), 0, 0.12, 0.15),
+        ],
+        dtype=[
+            ("time", "M8[ns]"),
+            ("MET", "i8"),
+            ("proton efficiency", "f8"),
+            ("alpha efficiency", "f8"),
+        ],
+    )
+    return table
+
+
+def _populate_shared(response, table):
+    chunk_fits._shared.update(swapi_response=response, efficiency_table=table)
+
+
+def _clear_shared():
+    chunk_fits._shared.clear()
+
+
+def _synthesize_chunk(*, response, rotations, proton_velocity_rtn, alpha_velocity_rtn, efficiency_table):
+    """Forward-model a 5-sweep proton + alpha chunk at the truth params over
+    the full 71-bin science axis. Per-species effective-area scales come from
+    `efficiency_table` so synthesis and the fitter share the same calibration."""
+    n = SWAPI_SCIENCE_BINS.stop - SWAPI_SCIENCE_BINS.start
+    voltages = np.tile(REALISTIC_ESA_VOLTAGES, _N_SWEEPS)
+
+    proton_ctx = build_solar_wind_fit_context(
+        count_rate=np.zeros(len(voltages)),
+        esa_voltage=voltages,
+        swapi_response=response,
+        central_effective_area_scale=chunk_fits._eff_scale(efficiency_table, _CHUNK_EPOCH, "proton"),
+        rotation_matrices=rotations,
+        mass_kg=PROTON_MASS_KG,
+        mass_per_charge_m_p_per_e=PROTON_MASS_PER_CHARGE_M_P_PER_E,
+    )
+    alpha_ctx = build_solar_wind_fit_context(
+        count_rate=np.zeros(len(voltages)),
+        esa_voltage=voltages,
+        swapi_response=response,
+        central_effective_area_scale=chunk_fits._eff_scale(efficiency_table, _CHUNK_EPOCH, "alpha"),
+        rotation_matrices=rotations,
+        mass_kg=ALPHA_PARTICLE_MASS_KG,
+        mass_per_charge_m_p_per_e=ALPHA_MASS_PER_CHARGE_M_P_PER_E,
+    )
+    proton_truth = SolarWindParams(
+        density=_TRUE_DENSITY,
+        bulk_velocity_rtn=proton_velocity_rtn.copy(),
+        temperature=_TRUE_TEMPERATURE_K,
+        mass=PROTON_MASS_KG,
+    )
+    alpha_truth = SolarWindParams(
+        density=_TRUE_ALPHA_DENSITY,
+        bulk_velocity_rtn=alpha_velocity_rtn.copy(),
+        temperature=_TRUE_ALPHA_TEMPERATURE_K,
+        mass=ALPHA_PARTICLE_MASS_KG,
+    )
+    proton_ideal, _ = model_solar_wind_ideal_coincidence_rates(proton_truth, proton_ctx)
+    alpha_ideal, _ = model_solar_wind_ideal_coincidence_rates(alpha_truth, alpha_ctx)
+    ideal = proton_ideal + alpha_ideal
+    flat_rates = ideal * deadtime_factor(ideal)
+    full_rates = np.zeros((_N_SWEEPS, _N_BINS))
+    full_rates[:, SWAPI_SCIENCE_BINS] = flat_rates.reshape(_N_SWEEPS, n)
+    chunk = SwapiL2Data(
+        sci_start_time=_SCI_START_TIME.copy(),
+        energy=np.tile(_FULL_ENERGY, (_N_SWEEPS, 1)),
+        coincidence_count_rate=full_rates,
+        coincidence_count_rate_uncertainty=np.full_like(full_rates, 0.1),
+    )
+    return chunk
+
+
+def _build_truth_chunk(response, efficiency_table):
+    """Forward-model a clean proton+alpha chunk from `response` over the science
+    bin range, returning the chunk plus the rotations and truth velocities used
+    to synthesize it. The alpha truth velocity is constructed here so all three
+    fitter test classes share the same proton-to-alpha relationship."""
+    science_rotations = _spice_rotations(SWAPI_SCIENCE_BINS)
+    proton_velocity_rtn = _truth_velocity_rtn(science_rotations)
+    alpha_velocity_rtn = proton_velocity_rtn + _TRUE_DELTA_V_KM_S * _B_HAT_RTN
+    chunk = _synthesize_chunk(
+        response=response,
+        rotations=science_rotations,
+        proton_velocity_rtn=proton_velocity_rtn,
+        alpha_velocity_rtn=alpha_velocity_rtn,
+        efficiency_table=efficiency_table,
+    )
+    return chunk, science_rotations, proton_velocity_rtn, alpha_velocity_rtn
+
+
+def _with_count_rate(chunk, count_rate):
+    return SwapiL2Data(
+        sci_start_time=chunk.sci_start_time,
+        energy=chunk.energy,
+        coincidence_count_rate=count_rate,
+        coincidence_count_rate_uncertainty=chunk.coincidence_count_rate_uncertainty,
+    )
+
+
+def _with_nan_at(chunk, sweep, bin_):
+    bad = chunk.coincidence_count_rate.copy()
+    bad[sweep, bin_] = np.nan
+    return _with_count_rate(chunk, bad)
+
+
+def _assert_all_nan(tc, result, scalar_keys, array_keys):
+    for key in scalar_keys:
+        tc.assertTrue(np.isnan(result[key]), msg=f"{key} not NaN")
+    for key in array_keys:
+        tc.assertTrue(np.all(np.isnan(result[key])), msg=f"{key} not all-NaN")
+
+
+def _assert_peak_speed_fallback(tc, result, scalar_keys, array_keys):
+    """Failure-mode contract for the proton fitter: `proton_sw_speed` is the
+    peak-bin ESA voltage converted to a cold-proton speed (within ~15 km/s of
+    `_TRUE_BULK_SPEED` for our forward-modelled chunk), and every other science
+    field NaN-fills."""
+    tc.assertAlmostEqual(result["proton_sw_speed"], _TRUE_BULK_SPEED, delta=15.0)
+    other_scalars = [k for k in scalar_keys if k != "proton_sw_speed"]
+    _assert_all_nan(tc, result, other_scalars, array_keys)
+
+
+def _assert_alpha_flag_and_all_nan(tc, result, flag):
+    tc.assertEqual(int(result["quality_flags"]), int(flag))
+    _assert_all_nan(tc, result, _ALPHA_SCALAR_KEYS, _ALPHA_ARRAY_KEYS)
+
+
+def _assert_proton_flag_and_peak_fallback(tc, result, flag):
+    tc.assertEqual(int(result["quality_flags"]), int(flag))
+    _assert_peak_speed_fallback(tc, result, _PROTON_SCALAR_KEYS, _PROTON_ARRAY_KEYS)
+
+
+def _zero_chunk():
+    return SwapiL2Data(
+        sci_start_time=np.array([_EPOCH_TT2000], dtype=np.int64),
+        energy=np.zeros((1, _N_BINS)),
+        coincidence_count_rate=np.zeros((1, _N_BINS)),
+        coincidence_count_rate_uncertainty=np.zeros((1, _N_BINS)),
+    )
+
+
+# 10**18 ns ≈ 31.7 years past `_EPOCH_TT2000` — beyond every kernel in
+# `spice_kernels/`, so SPICE raises `SpiceSPKINSUFFDATA` for this chunk.
+_OUT_OF_COVERAGE_START_TIME = _EPOCH_TT2000 + 10**18
+
+
+def _out_of_coverage_chunk():
+    return SwapiL2Data(
+        sci_start_time=np.array([_OUT_OF_COVERAGE_START_TIME], dtype=np.int64),
+        energy=np.zeros((1, _N_BINS)),
+        coincidence_count_rate=np.zeros((1, _N_BINS)),
+        coincidence_count_rate_uncertainty=np.zeros((1, _N_BINS)),
+    )
+
+
+# ----- ProtonChunkFitter ----------------------------------------------------
+
+
+class TestProtonChunkFitterPrecomputeGeometry(SpiceTestCase):
+    """Tests for `ProtonChunkFitter.precompute_geometry` with real SPICE kernels."""
+
+    def test_success_returns_epoch_rotations_and_sc_velocity(self):
+        """At an in-coverage chunk, precompute_geometry returns the chunk midpoint epoch, a per-bin rotation array of the right shape, and a 3-vector spacecraft velocity."""
+        epoch, rotation_matrices, spacecraft_velocity = ProtonChunkFitter().precompute_geometry(_zero_chunk())
+
+        self.assertEqual(epoch, _CHUNK_EPOCH)
+        assert rotation_matrices is not None and spacecraft_velocity is not None
+        self.assertEqual(rotation_matrices.shape, (_N_BINS - 1, 3, 3))
+        self.assertEqual(spacecraft_velocity.shape, (3,))
+
+    def test_spice_failure_yields_none_for_both_outputs(self):
+        """If the chunk falls outside SPICE coverage, the proton fitter returns None for both rotations and spacecraft velocity."""
+        _, rotation_matrices, spacecraft_velocity = ProtonChunkFitter().precompute_geometry(_out_of_coverage_chunk())
+
+        self.assertIsNone(rotation_matrices)
+        self.assertIsNone(spacecraft_velocity)
+
+
+class TestProtonChunkFitterFitChunk(SpiceTestCase):
+    """Tests for `ProtonChunkFitter.fit_chunk` — end-to-end proton fit plus
+    fill-value branches on a forward-modelled chunk. Uses real SPICE for
+    `rotate_rtn_to_dps` inside `derive_velocity_angles`."""
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.response = _swapi_response_with_warm_cache(np.tile(REALISTIC_ESA_VOLTAGES, _N_SWEEPS))
+        efficiency_table = _efficiency_table()
+        _populate_shared(cls.response, efficiency_table)
+        cls.chunk, cls.rotations, cls.true_proton_velocity_rtn, _ = _build_truth_chunk(cls.response, efficiency_table)
+        cls.result = ProtonChunkFitter().fit_chunk(
+            cls.chunk, _CHUNK_EPOCH, cls.rotations, _SC_VELOCITY_RTN.copy()
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        _clear_shared()
+        super().tearDownClass()
+
+    def test_quality_flag_none_and_epoch_passthrough(self):
+        """A clean synthetic chunk produces a NONE quality flag and passes the chunk epoch straight through to the result."""
+        self.assertEqual(self.result["quality_flags"], SwapiL3Flags.NONE)
+        self.assertEqual(self.result["epoch"], _CHUNK_EPOCH)
+
+    def test_recovers_truth_moments(self):
+        """Fitting a forward-modeled chunk back recovers the true density, temperature, and bulk velocity within a few percent."""
+        self.assertAlmostEqual(
+            self.result["proton_sw_density"], _TRUE_DENSITY, delta=0.05 * _TRUE_DENSITY
+        )
+        self.assertAlmostEqual(
+            self.result["proton_sw_temperature"],
+            _TRUE_TEMPERATURE_K,
+            delta=0.05 * _TRUE_TEMPERATURE_K,
+        )
+        np.testing.assert_allclose(
+            self.result["proton_sw_bulk_velocity_rtn_sc"], self.true_proton_velocity_rtn, atol=5.0
+        )
+
+    def test_uncertainties_are_strictly_positive(self):
+        """Every reported scalar uncertainty is strictly positive, so the LM Jacobian did not degenerate to zero."""
+        # Bit-exact zero would mean the LM Jacobian degenerated.
+        for key in _PROTON_SCALAR_KEYS:
+            if key.endswith("_uncert"):
+                with self.subTest(key=key):
+                    self.assertGreater(self.result[key], 0.0)
+
+    def test_speed_is_norm_of_sc_frame_velocity(self):
+        """Reported proton speed equals the magnitude of the SC-frame bulk velocity (rotation to DPS is norm-preserving)."""
+        np.testing.assert_allclose(
+            self.result["proton_sw_speed"],
+            np.linalg.norm(self.result["proton_sw_bulk_velocity_rtn_sc"]),
+            rtol=1e-9,
+        )
+
+    def test_sun_frame_velocity_is_sc_frame_plus_sc_velocity(self):
+        """Sun-frame velocity is the SC-frame velocity plus the SC orbital velocity, and Sun-frame speed is its magnitude."""
+        np.testing.assert_allclose(
+            self.result["proton_sw_bulk_velocity_rtn_sun"],
+            self.result["proton_sw_bulk_velocity_rtn_sc"] + _SC_VELOCITY_RTN,
+            atol=1e-9,
+        )
+        np.testing.assert_allclose(
+            self.result["proton_sw_speed_sun"],
+            np.linalg.norm(self.result["proton_sw_bulk_velocity_rtn_sun"]),
+            rtol=1e-9,
+        )
+
+    def test_speed_matches_magnitude_of_bulk_velocity_rtn(self):
+        """`proton_sw_speed` is the magnitude of the SC-frame RTN bulk velocity (magnitude is rotation-invariant)."""
+        np.testing.assert_allclose(
+            self.result["proton_sw_speed"],
+            np.linalg.norm(self.result["proton_sw_bulk_velocity_rtn_sc"]),
+            rtol=1e-12,
+        )
+
+    def test_velocity_covariance_is_symmetric_psd_and_sc_equals_sun(self):
+        """The 3x3 velocity covariance is symmetric and positive-semidefinite, and the same matrix is reported under both the spacecraft-frame and Sun-frame keys."""
+        covariance_spacecraft_frame = self.result["proton_sw_bulk_velocity_rtn_sc_covariance"]
+        covariance_sun_frame = self.result["proton_sw_bulk_velocity_rtn_sun_covariance"]
+        self.assertEqual(covariance_spacecraft_frame.shape, (3, 3))
+        np.testing.assert_allclose(covariance_spacecraft_frame, covariance_spacecraft_frame.T, atol=1e-12)
+        self.assertGreaterEqual(np.linalg.eigvalsh(covariance_spacecraft_frame)[0], 0.0)
+        # Source returns the same matrix for both keys.
+        np.testing.assert_array_equal(covariance_spacecraft_frame, covariance_sun_frame)
+
+    def test_missing_sc_velocity_fills_only_sun_frame_outputs(self):
+        """Calling fit_chunk with no SC velocity still runs the proton fit normally: density, temperature, SC-frame bulk velocity (and its covariance), peak speed, and DPS-frame clock/deflection angles are populated from the fit; only the sun-frame outputs (`proton_sw_speed_sun`, `proton_sw_bulk_velocity_rtn_sun`, and the sun-frame covariance) are fill values."""
+        result = ProtonChunkFitter().fit_chunk(
+            self.chunk, _CHUNK_EPOCH, self.rotations, None
+        )
+        self.assertEqual(result["quality_flags"], SwapiL3Flags.NONE)
+
+        self.assertTrue(np.isnan(result["proton_sw_speed_sun"]))
+        self.assertTrue(np.isnan(result["proton_sw_speed_sun_uncert"]))
+        self.assertTrue(np.all(np.isnan(result["proton_sw_bulk_velocity_rtn_sun"])))
+        self.assertTrue(
+            np.all(np.isnan(result["proton_sw_bulk_velocity_rtn_sun_covariance"]))
+        )
+
+        self.assertAlmostEqual(
+            result["proton_sw_density"], _TRUE_DENSITY, delta=0.05 * _TRUE_DENSITY
+        )
+        self.assertAlmostEqual(
+            result["proton_sw_temperature"],
+            _TRUE_TEMPERATURE_K,
+            delta=0.05 * _TRUE_TEMPERATURE_K,
+        )
+        np.testing.assert_allclose(
+            result["proton_sw_bulk_velocity_rtn_sc"],
+            self.true_proton_velocity_rtn,
+            atol=5.0,
+        )
+        self.assertTrue(
+            np.all(np.isfinite(result["proton_sw_bulk_velocity_rtn_sc_covariance"]))
+        )
+        self.assertTrue(np.isfinite(result["proton_sw_speed"]))
+
+
+# ----- AlphaChunkFitter -----------------------------------------------------
+
+
+class TestAlphaChunkFitterPrecomputeGeometry(SpiceTestCase):
+    """Tests for `AlphaChunkFitter.precompute_geometry` with real SPICE kernels."""
+
+    def _mag_centered_on(self, epoch_ns):
+        offsets = np.array([-1_000_000_000, 0, 1_000_000_000], dtype=np.int64)
+        return MagData(epoch=epoch_ns + offsets, mag_data=np.tile(_B_HAT_RTN, (3, 1)))
+
+    def test_success_returns_rotations_and_b_hat(self):
+        """With both SPICE and MAG available, alpha precompute_geometry returns the chunk epoch, a science-bin rotation array of the right shape (matching the proton fit it shares with proton-sw), and the median B̂ in the chunk window."""
+        epoch, rotation_matrices, b_hat = AlphaChunkFitter(
+            self._mag_centered_on(_CHUNK_EPOCH)
+        ).precompute_geometry(_zero_chunk())
+
+        self.assertEqual(epoch, _CHUNK_EPOCH)
+        assert rotation_matrices is not None
+        self.assertEqual(
+            rotation_matrices.shape,
+            (SWAPI_SCIENCE_BINS.stop - SWAPI_SCIENCE_BINS.start, 3, 3),
+        )
+        np.testing.assert_allclose(b_hat, _B_HAT_RTN)
+
+    def test_spice_failure_yields_none_rotations_but_keeps_b_hat(self):
+        """When the chunk falls outside SPICE coverage, alpha precompute returns None rotations but B̂ is still computed from MAG since that path is independent."""
+        out_of_coverage_chunk_epoch = _OUT_OF_COVERAGE_START_TIME + THIRTY_SECONDS_IN_NANOSECONDS
+        _, rotation_matrices, b_hat = AlphaChunkFitter(
+            self._mag_centered_on(out_of_coverage_chunk_epoch)
+        ).precompute_geometry(_out_of_coverage_chunk())
+        self.assertIsNone(rotation_matrices)
+        np.testing.assert_allclose(b_hat, _B_HAT_RTN)
+
+    def test_empty_mag_window_yields_nan_b_hat(self):
+        """When no MAG samples fall inside the chunk window, B̂ comes back as NaN even though rotations were successfully computed."""
+        far_future = _EPOCH_TT2000 + 10**18
+        _, _, b_hat = AlphaChunkFitter(
+            self._mag_centered_on(far_future)
+        ).precompute_geometry(_zero_chunk())
+        self.assertTrue(np.all(np.isnan(b_hat)))
+
+
+class TestAlphaChunkFitterFitChunk(SpiceTestCase):
+    """Tests for `AlphaChunkFitter.fit_chunk` — stage ordering plus fill-value branches in the alpha fit."""
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.response = _swapi_response_with_warm_cache(np.tile(REALISTIC_ESA_VOLTAGES, _N_SWEEPS))
+        efficiency_table = _efficiency_table()
+        _populate_shared(cls.response, efficiency_table)
+        cls.chunk, _, cls.true_proton_velocity_rtn, cls.true_alpha_velocity_rtn = _build_truth_chunk(cls.response, efficiency_table)
+        # AlphaChunkFitter and ProtonChunkFitter share the same proton fit on
+        # `SWAPI_SCIENCE_BINS`, so the rotations passed in must span the full
+        # science range; AlphaChunkFitter slices down to coarse for Stage 2.
+        cls.rotations = _spice_rotations(SWAPI_SCIENCE_BINS)
+        cls.fitter = AlphaChunkFitter(mag_data=None)
+        cls.happy_result = cls.fitter.fit_chunk(
+            cls.chunk, _CHUNK_EPOCH, cls.rotations, _B_HAT_RTN
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        _clear_shared()
+        super().tearDownClass()
+
+    def test_recovers_alpha_truth_moments(self):
+        """Fitting a forward-modeled proton+alpha chunk recovers the true alpha density, temperature, delta-v, and bulk velocity within a few percent."""
+        self.assertAlmostEqual(
+            self.happy_result["alpha_sw_density"],
+            _TRUE_ALPHA_DENSITY,
+            delta=0.10 * _TRUE_ALPHA_DENSITY,
+        )
+        self.assertAlmostEqual(
+            self.happy_result["alpha_sw_temperature"],
+            _TRUE_ALPHA_TEMPERATURE_K,
+            delta=0.10 * _TRUE_ALPHA_TEMPERATURE_K,
+        )
+        self.assertAlmostEqual(
+            self.happy_result["alpha_sw_delta_v"], _TRUE_DELTA_V_KM_S, delta=2.0
+        )
+        np.testing.assert_allclose(
+            self.happy_result["alpha_sw_velocity_rtn"], self.true_alpha_velocity_rtn, atol=5.0
+        )
+
+    def test_b_hat_passes_through_to_result(self):
+        """The B̂ argument is surfaced unchanged under `alpha_sw_b_hat_rtn`."""
+        np.testing.assert_array_equal(self.happy_result["alpha_sw_b_hat_rtn"], _B_HAT_RTN)
+
+    def test_quality_flag_none_on_clean_chunk(self):
+        """A clean chunk yields a NONE quality flag (both Stage 1 and Stage 2 converged)."""
+        self.assertEqual(
+            int(self.happy_result["quality_flags"]), int(SwapiL3Flags.NONE)
+        )
+
+
+# ----- ParallelChunkRunner --------------------------------------------------
+
+
+class _RecordingChunkFitter(ChunkFitter):
+    """A real `ChunkFitter` that echoes the chunk's start time. Module-level
+    so fork-spawned workers can locate it via inherited memory."""
+
+    def precompute_geometry(self, chunk):
+        return (int(chunk.sci_start_time[0]),)
+
+    def fit_chunk(self, chunk, epoch):
+        return {
+            "epoch": epoch,
+            "first_start_time": int(chunk.sci_start_time[0]),
+        }
+
+
+class _WarmCacheProbeChunkFitter(ChunkFitter):
+    """A real `ChunkFitter` whose `fit_chunk` reports the `_passband_grid_cache` size
+    seen by the worker process. Module-level for fork inheritance."""
+
+    def precompute_geometry(self, chunk):
+        return (int(chunk.sci_start_time[0]),)
+
+    def fit_chunk(self, chunk, epoch):
+        worker_response = chunk_fits._shared["swapi_response"]
+        return {
+            "epoch": epoch,
+            "worker_cache_size": len(worker_response._passband_grid_cache),
+        }
+
+
+def _make_chunk_with_start_time(start_time):
+    return SwapiL2Data(
+        sci_start_time=np.array([start_time], dtype=np.int64),
+        energy=np.zeros((1, _N_BINS)),
+        coincidence_count_rate=np.zeros((1, _N_BINS)),
+        coincidence_count_rate_uncertainty=np.zeros((1, _N_BINS)),
+    )
+
+
+@unittest.skipIf(platform.system() == "Windows", "fork is not available on Windows")
+class TestParallelChunkRunnerOrchestration(unittest.TestCase):
+    """Tests for `ParallelChunkRunner.run` against a real fork-based Pool."""
+
+    def tearDown(self):
+        _clear_shared()
+
+    def test_dispatches_per_chunk_and_stacks_outputs_in_chunk_order(self):
+        """Two chunks with distinct start times produce a stacked output where each per-key array preserves chunk order across the real fork pool."""
+        chunks = [
+            _make_chunk_with_start_time(_EPOCH_TT2000),
+            _make_chunk_with_start_time(_EPOCH_TT2000 + 12_000_000_000),
+        ]
+        runner = ParallelChunkRunner(
+            swapi_response=_swapi_response_with_warm_cache(np.tile(REALISTIC_ESA_VOLTAGES, _N_SWEEPS)),
+            efficiency_table=_efficiency_table(),
+        )
+
+        result = runner.run(chunks, _RecordingChunkFitter())
+
+        expected_epochs = np.array(
+            [int(chunks[0].sci_start_time[0]), int(chunks[1].sci_start_time[0])]
+        )
+        np.testing.assert_array_equal(result["epoch"], expected_epochs)
+        np.testing.assert_array_equal(result["first_start_time"], expected_epochs)
+
+    def test_workers_see_parent_warm_cache_under_fork(self):
+        """A passband grid cache populated in the parent before `runner.run` is visible inside each fork-spawned worker at the same size."""
+        voltages = np.array([10.0, 50.0, 100.0])
+        response = _swapi_response_with_warm_cache(voltages)
+        parent_cache_size = len(response._passband_grid_cache)
+        self.assertEqual(parent_cache_size, len(voltages))
+
+        runner = ParallelChunkRunner(
+            swapi_response=response, efficiency_table=_efficiency_table()
+        )
+
+        result = runner.run(
+            [_make_chunk_with_start_time(_EPOCH_TT2000)], _WarmCacheProbeChunkFitter()
+        )
+
+
+        np.testing.assert_array_equal(
+            result["worker_cache_size"], np.array([parent_cache_size])
+        )
+
+
+# ----- Proton quality flags -------------------------------------------------
+
+
+class TestProtonChunkFitterQualityFlags(SpiceTestCase):
+    """Quality-flag branches of `ProtonChunkFitter.fit_chunk`, ordered to match `docs/swapi/solar-wind-moments.md` §Quality flags: `BAD_FIT`, `FIT_ERROR`, then NONE-flag data-gap fills."""
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.response = _swapi_response_with_warm_cache(np.tile(REALISTIC_ESA_VOLTAGES, _N_SWEEPS))
+        efficiency_table = _efficiency_table()
+        _populate_shared(cls.response, efficiency_table)
+        cls.chunk, cls.rotations, _, _ = _build_truth_chunk(cls.response, efficiency_table)
+        cls.fitter = ProtonChunkFitter()
+
+    @classmethod
+    def tearDownClass(cls):
+        _clear_shared()
+        super().tearDownClass()
+
+    def _fit(self, chunk):
+        return self.fitter.fit_chunk(
+            chunk, _CHUNK_EPOCH, self.rotations, _SC_VELOCITY_RTN.copy()
+        )
+
+    def test_bad_fit_when_peak_does_not_match_maxwellian(self):
+        """Scrambling the proton peak bins yields a spectrum the LM fit cannot describe (the BAD_FIT quality guard fires); the chunk fitter surfaces `BAD_FIT` with peak-bin speed and NaN-filled moments."""
+        rng = np.random.default_rng(0)
+        mean_count = self.chunk.coincidence_count_rate.mean(axis=0)
+        peak_bin = int(np.argmax(mean_count))
+        peak_window = slice(peak_bin - 2, peak_bin + 3)
+        permuted = rng.permutation(np.arange(peak_window.start, peak_window.stop))
+        corrupted = self.chunk.coincidence_count_rate.copy()
+        corrupted[:, peak_window] = corrupted[:, permuted]
+
+        result = self._fit(_with_count_rate(self.chunk, corrupted))
+
+        _assert_proton_flag_and_peak_fallback(self, result, SwapiL3Flags.BAD_FIT)
+
+    @patch("imap_l3_processing.swapi.l3a.chunk_fits._fit_proton")
+    def test_fit_error_when_inner_fit_returns_fit_error(self, mock_fit_proton):
+        """When the inner proton fit returns `FIT_ERROR` with NaN moments (scipy LM reported `success=False`), the chunk fitter propagates the flag, falls back to peak-bin speed, and NaN-fills every other science field. Mocked because there is no clean way to force scipy LM to report `success=False` from real inputs."""
+        nan = ufloat(np.nan, np.nan)
+        mock_fit_proton.return_value = ProtonSolarWindFitResult(
+            density=nan,
+            temperature=nan,
+            bulk_velocity_rtn=(nan, nan, nan),
+            bad_fit_flag=int(SwapiL3Flags.FIT_ERROR),
+        )
+
+        result = self._fit(self.chunk)
+
+        _assert_proton_flag_and_peak_fallback(self, result, SwapiL3Flags.FIT_ERROR)
+
+    @patch("imap_l3_processing.swapi.l3a.science.solar_wind.proton.fit_model.calculate_initial_guess")
+    def test_fit_error_when_initial_guess_is_nan(self, mock_initial_guess):
+        """A NaN-valued initial guess causes scipy `least_squares` to reject `x0` as infeasible; the chunk fitter catches the exception, surfaces `FIT_ERROR`, and falls back to peak-bin speed. Mocked because `calculate_initial_guess` never returns NaN from real inputs (it raises instead)."""
+        mock_initial_guess.return_value = SolarWindParams(
+            density=np.nan,
+            bulk_velocity_rtn=np.full(3, np.nan),
+            temperature=np.nan,
+            mass=PROTON_MASS_KG,
+        )
+
+        result = self._fit(self.chunk)
+
+        _assert_proton_flag_and_peak_fallback(self, result, SwapiL3Flags.FIT_ERROR)
+
+    def test_fit_error_when_initial_guess_raises(self):
+        """A spectrum with a single non-zero bin (no Gaussian shape to fit) makes scipy `curve_fit` raise inside the initial-guess Gaussian refine; the chunk fitter catches the exception, surfaces `FIT_ERROR`, and falls back to peak-bin speed."""
+        peak_bin = int(np.argmax(self.chunk.coincidence_count_rate.mean(axis=0)))
+        single_bin = np.zeros_like(self.chunk.coincidence_count_rate)
+        single_bin[:, peak_bin] = 100.0
+
+        result = self._fit(_with_count_rate(self.chunk, single_bin))
+
+        _assert_proton_flag_and_peak_fallback(self, result, SwapiL3Flags.FIT_ERROR)
+
+    def test_no_flag_when_rotations_missing(self):
+        """Calling fit_chunk with no rotations reports a NONE quality flag (ephemeris gaps are treated as data gaps without a dedicated flag) and falls back to peak-bin ESA voltage as `proton_sw_speed`; every other science field NaN-fills."""
+        result = self.fitter.fit_chunk(self.chunk, _CHUNK_EPOCH, None, _SC_VELOCITY_RTN)
+        _assert_proton_flag_and_peak_fallback(self, result, SwapiL3Flags.NONE)
+
+    def test_no_flag_when_count_rate_has_nan(self):
+        """A NaN in the count rate is treated as an L2 data gap: the quality flag is NONE, `proton_sw_speed` reports the peak-bin speed (taken across finite bins via `nanargmax`), and every other science field NaN-fills."""
+        result = self._fit(_with_nan_at(self.chunk, 0, 5))
+        _assert_proton_flag_and_peak_fallback(self, result, SwapiL3Flags.NONE)
+
+
+# ----- Alpha quality flags --------------------------------------------------
+
+
+class TestAlphaChunkFitterQualityFlags(SpiceTestCase):
+    """Quality-flag branches of `AlphaChunkFitter.fit_chunk` and the `PRELIMINARY_MAG` bit-OR in `SwapiProcessor.process_l3a_alpha`, ordered to match `docs/swapi/solar-wind-moments.md` §Quality flags: `BAD_FIT`, `PRELIMINARY_MAG`, then NONE-flag data-gap fills."""
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.response = _swapi_response_with_warm_cache(np.tile(REALISTIC_ESA_VOLTAGES, _N_SWEEPS))
+        efficiency_table = _efficiency_table()
+        _populate_shared(cls.response, efficiency_table)
+        cls.chunk, cls.rotations, _, _ = _build_truth_chunk(cls.response, efficiency_table)
+        cls.fitter = AlphaChunkFitter(mag_data=None)
+
+    @classmethod
+    def tearDownClass(cls):
+        _clear_shared()
+        super().tearDownClass()
+
+    def test_bad_fit_when_alpha_bump_does_not_match_maxwellian(self):
+        """Scrambling and amplifying the alpha-bump bins leaves the proton peak intact but yields a Stage-2 residual the alpha LM cannot describe (the BAD_FIT quality guard fires); the chunk fitter surfaces `BAD_FIT` with every alpha moment NaN-filled while B̂ passes through unchanged."""
+        rng = np.random.default_rng(0)
+        permuted = rng.permutation(np.arange(_ALPHA_BUMP_BINS.start, _ALPHA_BUMP_BINS.stop))
+        corrupted = self.chunk.coincidence_count_rate.copy()
+        corrupted[:, _ALPHA_BUMP_BINS] = corrupted[:, permuted] * 3.0
+
+        result = self.fitter.fit_chunk(
+            _with_count_rate(self.chunk, corrupted),
+            _CHUNK_EPOCH,
+            self.rotations,
+            _B_HAT_RTN,
+        )
+
+        non_b_hat_array_keys = [k for k in _ALPHA_ARRAY_KEYS if k != "alpha_sw_b_hat_rtn"]
+        self.assertEqual(int(result["quality_flags"]), int(SwapiL3Flags.BAD_FIT))
+        _assert_all_nan(self, result, _ALPHA_SCALAR_KEYS, non_b_hat_array_keys)
+        np.testing.assert_array_equal(result["alpha_sw_b_hat_rtn"], _B_HAT_RTN)
+
+    @patch("imap_l3_processing.swapi.swapi_processor.SwapiL3AlphaSolarWindData")
+    @patch("imap_l3_processing.swapi.swapi_processor.ParallelChunkRunner")
+    @patch("imap_l3_processing.swapi.swapi_processor.chunk_l2_data")
+    def test_preliminary_mag_bit_set_iff_mag_is_preliminary(
+        self, mock_chunk_l2_data, mock_runner_class, mock_alpha_data_class
+    ):
+        """The `PRELIMINARY_MAG` bit is OR'd onto every per-chunk quality flag when `mag_is_preliminary=True` (preserving any underlying `BAD_FIT`), and the per-chunk flags pass through untouched when `mag_is_preliminary=False`. Mocked at the processor seam because the bit-OR is processor-level wiring, not chunk-fitter behavior."""
+        runner_flag = int(SwapiL3Flags.BAD_FIT)
+        cases = [
+            (True, runner_flag | int(SwapiL3Flags.PRELIMINARY_MAG)),
+            (False, runner_flag),
+        ]
+        for mag_is_preliminary, expected_flag in cases:
+            with self.subTest(mag_is_preliminary=mag_is_preliminary):
+                mock_chunk_l2_data.return_value = [Mock()]
+                mock_runner_class.return_value.run.return_value = {
+                    "quality_flags": np.array([runner_flag])
+                }
+                dependencies = Mock(
+                    mag_data=Mock(),
+                    mag_is_preliminary=mag_is_preliminary,
+                    swapi_response=Mock(),
+                    efficiency_calibration_table=Mock(),
+                )
+                data = Mock(energy=np.array([[1000.0, 2000.0]]))
+                metadata = InputMetadata(
+                    "swapi", "l3a", datetime(2025, 1, 1), datetime(2025, 1, 2), "v001"
+                )
+
+                SwapiProcessor(Mock(), metadata).process_l3a_alpha(data, dependencies)
+
+                passed_quality_flags = mock_alpha_data_class.call_args.kwargs[
+                    "quality_flags"
+                ]
+                np.testing.assert_array_equal(passed_quality_flags, [expected_flag])
+
+    def test_no_flag_when_rotations_missing(self):
+        """Calling fit_chunk with no rotations NaN-fills every alpha field and reports a NONE bad_fit_flag — ephemeris gaps are treated as data gaps without a dedicated flag."""
+        result = self.fitter.fit_chunk(self.chunk, _CHUNK_EPOCH, None, _B_HAT_RTN)
+        _assert_alpha_flag_and_all_nan(self, result, SwapiL3Flags.NONE)
+
+    def test_no_flag_when_b_hat_is_nan(self):
+        """A NaN-valued B̂ NaN-fills every alpha field and reports a NONE bad_fit_flag — MAG gaps are treated as data gaps without a dedicated flag, since the field-aligned drift constraint cannot be evaluated."""
+        result = self.fitter.fit_chunk(
+            self.chunk, _CHUNK_EPOCH, self.rotations, np.full(3, np.nan)
+        )
+        _assert_alpha_flag_and_all_nan(self, result, SwapiL3Flags.NONE)
+
+    def test_no_flag_when_b_hat_is_none(self):
+        """Passing None for B̂ NaN-fills every alpha field and reports a NONE bad_fit_flag, mirroring the NaN case."""
+        result = self.fitter.fit_chunk(self.chunk, _CHUNK_EPOCH, self.rotations, None)
+        _assert_alpha_flag_and_all_nan(self, result, SwapiL3Flags.NONE)
+
+    def test_no_flag_when_count_rate_has_nan(self):
+        """A NaN in the alpha count rate is treated as an L2 data gap: every alpha field NaN-fills and bad_fit_flag is NONE."""
+        result = self.fitter.fit_chunk(
+            _with_nan_at(self.chunk, 0, 5), _CHUNK_EPOCH, self.rotations, _B_HAT_RTN
+        )
+        _assert_alpha_flag_and_all_nan(self, result, SwapiL3Flags.NONE)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/l3a/test_models.py b/tests/swapi/l3a/test_models.py
index 61033f649..27454d7fb 100644
--- a/tests/swapi/l3a/test_models.py
+++ b/tests/swapi/l3a/test_models.py
@@ -6,12 +6,20 @@
 from imap_l3_processing.constants import THIRTY_SECONDS_IN_NANOSECONDS, FIVE_MINUTES_IN_NANOSECONDS
 from imap_l3_processing.swapi.l3a.models import SwapiL3ProtonSolarWindData, EPOCH_CDF_VAR_NAME, \
     PROTON_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME, PROTON_SOLAR_WIND_SPEED_CDF_VAR_NAME, EPOCH_DELTA_CDF_VAR_NAME, \
-    SwapiL3AlphaSolarWindData, ALPHA_SOLAR_WIND_SPEED_CDF_VAR_NAME, \
-    ALPHA_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME, PROTON_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME, \
+    PROTON_SOLAR_WIND_SPEED_SUN_CDF_VAR_NAME, PROTON_SOLAR_WIND_SPEED_SUN_UNCERTAINTY_CDF_VAR_NAME, \
+    SwapiL3AlphaSolarWindData, PROTON_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME, \
     PROTON_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME, PROTON_SOLAR_WIND_DENSITY_CDF_VAR_NAME, \
-    PROTON_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME, PROTON_SOLAR_WIND_CLOCK_ANGLE_CDF_VAR_NAME, \
-    PROTON_SOLAR_WIND_CLOCK_ANGLE_UNCERTAINTY_CDF_VAR_NAME, PROTON_SOLAR_WIND_DEFLECTION_ANGLE_CDF_VAR_NAME, \
-    PROTON_SOLAR_WIND_DEFLECTION_ANGLE_UNCERTAINTY_CDF_VAR_NAME, SwapiL3PickupIonData, PUI_COOLING_INDEX_CDF_VAR_NAME, \
+    PROTON_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME, \
+    PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SUN_CDF_VAR_NAME, \
+    PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SUN_COVARIANCE_CDF_VAR_NAME, \
+    PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SC_CDF_VAR_NAME, \
+    PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SC_COVARIANCE_CDF_VAR_NAME, \
+    ALPHA_SOLAR_WIND_DENSITY_CDF_VAR_NAME, ALPHA_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME, \
+    ALPHA_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME, ALPHA_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME, \
+    ALPHA_SOLAR_WIND_VELOCITY_RTN_CDF_VAR_NAME, ALPHA_SOLAR_WIND_VELOCITY_COVARIANCE_RTN_CDF_VAR_NAME, \
+    ALPHA_SOLAR_WIND_DELTA_V_CDF_VAR_NAME, ALPHA_SOLAR_WIND_DELTA_V_UNCERT_CDF_VAR_NAME, \
+    ALPHA_SOLAR_WIND_B_HAT_RTN_CDF_VAR_NAME, \
+    SwapiL3PickupIonData, PUI_COOLING_INDEX_CDF_VAR_NAME, \
     PUI_IONIZATION_RATE_CDF_VAR_NAME, PUI_CUTOFF_SPEED_CDF_VAR_NAME, PUI_BACKGROUND_COUNT_RATE_CDF_VAR_NAME, \
     PUI_DENSITY_CDF_VAR_NAME, PUI_TEMPERATURE_CDF_VAR_NAME, PUI_COOLING_INDEX_UNCERTAINTY_CDF_VAR_NAME, \
     PUI_IONIZATION_RATE_UNCERTAINTY_CDF_VAR_NAME, PUI_CUTOFF_SPEED_UNCERTAINTY_CDF_VAR_NAME, \
@@ -26,97 +34,104 @@ class TestModels(CdfModelTestCase):
     def test_getting_proton_sw_data_product_variables(self):
         epoch_data = np.arange(20, step=2)
         epoch_delta = np.full_like(epoch_data, THIRTY_SECONDS_IN_NANOSECONDS)
-        expected_nominal_values = np.arange(10, step=1.0)
-        expected_std = np.arange(5, step=.5)
-        proton_speed = uarray(expected_nominal_values, expected_std)
-        expected_temperature_nominal_values = np.arange(1000, 2000, step=100.)
-        expected_temperature_std = np.arange(50, step=5.)
-        temperature_data = uarray(expected_temperature_nominal_values, expected_temperature_std)
-        expected_density_nominal_values = np.arange(3, 13, step=1.)
-        expected_density_std = np.arange(1, step=.1)
-        density_data = uarray(expected_density_nominal_values, expected_density_std)
-        expected_clock_angle = np.arange(10, step=1.)
-        expected_clock_angle_std = np.arange(2, step=.2)
-        clock_angle_data = uarray(expected_clock_angle, expected_clock_angle_std)
-        expected_flow_deflection = np.arange(100, step=10.)
-        expected_flow_deflection_std = np.arange(1, step=.1)
-        flow_deflection_data = uarray(expected_flow_deflection, expected_flow_deflection_std)
-        quality_flags = np.full(20, SwapiL3Flags.NONE)
-        quality_flags[3:5] |= SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED
-        data = SwapiL3ProtonSolarWindData(Mock(), epoch_data, proton_speed, temperature_data, density_data,
-                                          clock_angle_data,
-                                          flow_deflection_data, quality_flags)
+        n = len(epoch_data)
+
+        speed = np.arange(10, step=1.0)
+        speed_uncert = np.arange(5, step=.5)
+        speed_sun = np.arange(20, 30, step=1.0)
+        speed_sun_uncert = np.arange(2, 7, step=.5)
+        temperature = np.arange(1000, 2000, step=100.)
+        temperature_uncert = np.arange(50, step=5.)
+        density = np.arange(3, 13, step=1.)
+        density_uncert = np.arange(1, step=.1)
+        bulk_v_rtn_sun = np.arange(n * 3, dtype=float).reshape(n, 3)
+        bulk_v_rtn_sun_cov = np.arange(n * 9, dtype=float).reshape(n, 3, 3)
+        bulk_v_rtn_sc = np.arange(100, 100 + n * 3, dtype=float).reshape(n, 3)
+        bulk_v_rtn_sc_cov = np.arange(200, 200 + n * 9, dtype=float).reshape(n, 3, 3)
+
+        quality_flags = np.full(n, SwapiL3Flags.NONE)
+        quality_flags[3:5] |= SwapiL3Flags.FIT_ERROR
+
+        data = SwapiL3ProtonSolarWindData(
+            Mock(), epoch_data,
+            speed, speed_uncert,
+            speed_sun, speed_sun_uncert,
+            temperature, temperature_uncert,
+            density, density_uncert,
+            bulk_v_rtn_sun, bulk_v_rtn_sun_cov,
+            bulk_v_rtn_sc, bulk_v_rtn_sc_cov,
+            quality_flags,
+        )
 
         variables = data.to_data_product_variables()
 
         self.assert_variable_attributes(variables[0], epoch_data, EPOCH_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[1], expected_nominal_values, PROTON_SOLAR_WIND_SPEED_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[2], expected_std, PROTON_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[3], epoch_delta, EPOCH_DELTA_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[4], expected_temperature_nominal_values,
-                                        PROTON_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[5], expected_temperature_std,
+        self.assert_variable_attributes(variables[1], speed, PROTON_SOLAR_WIND_SPEED_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[2], speed_uncert, PROTON_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[3], speed_sun, PROTON_SOLAR_WIND_SPEED_SUN_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[4], speed_sun_uncert,
+                                        PROTON_SOLAR_WIND_SPEED_SUN_UNCERTAINTY_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[5], epoch_delta, EPOCH_DELTA_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[6], temperature, PROTON_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[7], temperature_uncert,
                                         PROTON_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[6], expected_density_nominal_values,
-                                        PROTON_SOLAR_WIND_DENSITY_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[7], expected_density_std,
+        self.assert_variable_attributes(variables[8], density, PROTON_SOLAR_WIND_DENSITY_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[9], density_uncert,
                                         PROTON_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[8], expected_clock_angle, PROTON_SOLAR_WIND_CLOCK_ANGLE_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[9], expected_clock_angle_std,
-                                        PROTON_SOLAR_WIND_CLOCK_ANGLE_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[10], expected_flow_deflection,
-                                        PROTON_SOLAR_WIND_DEFLECTION_ANGLE_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[11], expected_flow_deflection_std,
-                                        PROTON_SOLAR_WIND_DEFLECTION_ANGLE_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[12], quality_flags, SWAPI_QUALITY_FLAGS_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[10], bulk_v_rtn_sun,
+                                        PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SUN_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[11], bulk_v_rtn_sun_cov,
+                                        PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SUN_COVARIANCE_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[12], bulk_v_rtn_sc,
+                                        PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SC_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[13], bulk_v_rtn_sc_cov,
+                                        PROTON_SOLAR_WIND_BULK_VELOCITY_RTN_SC_COVARIANCE_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[14], quality_flags, SWAPI_QUALITY_FLAGS_CDF_VAR_NAME)
 
     def test_getting_alpha_sw_data_product_variables(self):
         epoch_data = np.arange(20, step=2)
         epoch_delta = np.full_like(epoch_data, THIRTY_SECONDS_IN_NANOSECONDS)
-        expected_speed_nominal_values = np.arange(10, step=1.)
-        expected_speed_std = np.arange(5, step=.5)
-        alpha_speed = uarray(expected_speed_nominal_values, expected_speed_std)
-        expected_temperature_nominal_values = np.arange(300000, step=30000.)
-        expected_temperature_std_devs = np.arange(50000, step=5000.)
-        alpha_temperature = uarray(expected_temperature_nominal_values, expected_temperature_std_devs)
-        expected_alpha_density_nominal_values = np.arange(2, step=.2)
-        expected_alpha_density_std_devs = np.arange(1, step=0.1)
-        expected_flag_values = np.full_like(epoch_data, SwapiL3Flags.NONE)
-        expected_flag_values[:len(epoch_data) // 2] = SwapiL3Flags.HI_CHI_SQ
-
-        alpha_density = uarray(expected_alpha_density_nominal_values, expected_alpha_density_std_devs)
-
-        expected_pre_lut_temperature_nominal_values = np.arange(400000, step=40000.)
-        expected_pre_lut_temperature_std_devs = np.arange(20000, step=2000.)
-        alpha_pre_lut_temperature = uarray(expected_pre_lut_temperature_nominal_values, expected_pre_lut_temperature_std_devs)
-        expected_pre_lut_alpha_density_nominal_values = np.arange(2.4, step=.24)
-        expected_pre_lut_alpha_density_std_devs = np.arange(1.5, step=0.15)
-        alpha_pre_lut_density = uarray(expected_pre_lut_alpha_density_nominal_values, expected_pre_lut_alpha_density_std_devs)
-
-        data = SwapiL3AlphaSolarWindData(Mock(), epoch_data, alpha_speed, alpha_temperature, alpha_density,
-                                         expected_flag_values, alpha_pre_lut_temperature, alpha_pre_lut_density)
+        n = len(epoch_data)
+
+        density = np.arange(2, step=.2)
+        density_uncert = np.arange(1, step=0.1)
+        temperature = np.arange(300000, step=30000.)
+        temperature_uncert = np.arange(50000, step=5000.)
+        velocity_rtn = np.arange(n * 3, dtype=float).reshape(n, 3)
+        velocity_cov_rtn = np.arange(n * 9, dtype=float).reshape(n, 3, 3)
+        delta_v = np.arange(10, step=1.)
+        delta_v_uncert = np.arange(5, step=.5)
+        b_hat_rtn = np.arange(100, 100 + n * 3, dtype=float).reshape(n, 3)
+
+        quality_flags = np.full_like(epoch_data, SwapiL3Flags.NONE)
+        quality_flags[:n // 2] = SwapiL3Flags.BAD_FIT
+
+        data = SwapiL3AlphaSolarWindData(
+            Mock(), epoch_data,
+            density, density_uncert,
+            temperature, temperature_uncert,
+            velocity_rtn, velocity_cov_rtn,
+            delta_v, delta_v_uncert,
+            b_hat_rtn,
+            quality_flags,
+        )
         variables = data.to_data_product_variables()
 
         self.assert_variable_attributes(variables[0], epoch_data, EPOCH_CDF_VAR_NAME)
         self.assert_variable_attributes(variables[1], epoch_delta, EPOCH_DELTA_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[2], expected_speed_nominal_values,
-                                        ALPHA_SOLAR_WIND_SPEED_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[3], expected_speed_std,
-                                        ALPHA_SOLAR_WIND_SPEED_UNCERTAINTY_CDF_VAR_NAME)
-        self.assert_variable_attributes(variables[4], expected_temperature_nominal_values,
-                                        "alpha_sw_temperature")
-        self.assert_variable_attributes(variables[5], expected_temperature_std_devs,
-                                        "alpha_sw_temperature_uncert")
-        self.assert_variable_attributes(variables[6], expected_alpha_density_nominal_values,
-                                        "alpha_sw_density")
-        self.assert_variable_attributes(variables[7], expected_alpha_density_std_devs,
-                                        "alpha_sw_density_uncert")
-
-        self.assert_variable_attributes(variables[8], expected_flag_values, "swp_flags")
-
-        self.assert_variable_attributes(variables[9], expected_pre_lut_temperature_nominal_values, "alpha_sw_pre_lut_temperature")
-        self.assert_variable_attributes(variables[10], expected_pre_lut_alpha_density_nominal_values, "alpha_sw_pre_lut_density")
-
+        self.assert_variable_attributes(variables[2], density, ALPHA_SOLAR_WIND_DENSITY_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[3], density_uncert,
+                                        ALPHA_SOLAR_WIND_DENSITY_UNCERTAINTY_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[4], temperature, ALPHA_SOLAR_WIND_TEMPERATURE_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[5], temperature_uncert,
+                                        ALPHA_SOLAR_WIND_TEMPERATURE_UNCERTAINTY_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[6], velocity_rtn, ALPHA_SOLAR_WIND_VELOCITY_RTN_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[7], velocity_cov_rtn,
+                                        ALPHA_SOLAR_WIND_VELOCITY_COVARIANCE_RTN_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[8], delta_v, ALPHA_SOLAR_WIND_DELTA_V_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[9], delta_v_uncert, ALPHA_SOLAR_WIND_DELTA_V_UNCERT_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[10], b_hat_rtn, ALPHA_SOLAR_WIND_B_HAT_RTN_CDF_VAR_NAME)
+        self.assert_variable_attributes(variables[11], quality_flags, SWAPI_QUALITY_FLAGS_CDF_VAR_NAME)
 
     def test_getting_pui_data_product_variables(self):
         epoch_data = np.arange(20, step=2)
diff --git a/tests/swapi/l3a/test_swapi_l3a_dependencies.py b/tests/swapi/l3a/test_swapi_l3a_dependencies.py
index 8495c461a..21e26934d 100644
--- a/tests/swapi/l3a/test_swapi_l3a_dependencies.py
+++ b/tests/swapi/l3a/test_swapi_l3a_dependencies.py
@@ -5,21 +5,21 @@
 import imap_data_access
 from imap_data_access.processing_input import ScienceInput, ProcessingInputCollection, AncillaryInput
 
-from imap_l3_processing.swapi.descriptors import SWAPI_L2_DESCRIPTOR, ALPHA_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR, \
-    PROTON_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR, \
+from imap_l3_processing.swapi.descriptors import SWAPI_L2_DESCRIPTOR, \
     INSTRUMENT_RESPONSE_LOOKUP_TABLE_DESCRIPTOR, DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR, \
-    CLOCK_ANGLE_AND_FLOW_DEFLECTION_LOOKUP_TABLE_DESCRIPTOR, EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR, \
-    GEOMETRIC_FACTOR_PUI_LOOKUP_TABLE_DESCRIPTOR, HYDROGEN_INFLOW_VECTOR_DESCRIPTOR, HELIUM_INFLOW_VECTOR_DESCRIPTOR
+    EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR, \
+    GEOMETRIC_FACTOR_PUI_LOOKUP_TABLE_DESCRIPTOR, HYDROGEN_INFLOW_VECTOR_DESCRIPTOR, HELIUM_INFLOW_VECTOR_DESCRIPTOR, \
+    AZIMUTHAL_TRANSMISSION_DESCRIPTOR, CENTRAL_EFFECTIVE_AREA_DESCRIPTOR, PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR, \
+    MAG_RTN_DESCRIPTOR
 from imap_l3_processing.swapi.l3a.swapi_l3a_dependencies import SwapiL3ADependencies
 
 
 class TestSwapiL3ADependencies(unittest.TestCase):
 
-    @patch(
-        "imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.SwapiL3ADependencies.from_file_paths")
-    @patch(
-        "imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.download")
-    def test_fetch_dependencies(self, mock_download, mock_from_file_paths):
+    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.select_mag_path")
+    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.SwapiL3ADependencies.from_file_paths")
+    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.download")
+    def test_fetch_dependencies(self, mock_download, mock_from_file_paths, mock_select_mag_path):
         input_collection = ProcessingInputCollection()
 
         start_date = '20100105'
@@ -30,47 +30,49 @@ def test_fetch_dependencies(self, mock_download, mock_from_file_paths):
 
         mock_download.side_effect = [
             sentinel.swapi_l2_data,
-            sentinel.proton_density_and_temperature_calibration_file,
-            sentinel.alpha_density_and_temperature_calibration_file,
-            sentinel.clock_and_deflection_file,
             sentinel.efficiency_file,
             sentinel.geometric_factor_calibration_table,
             sentinel.instrument_response_table,
             sentinel.neutral_helium_table,
             sentinel.hydrogen_vector,
             sentinel.helium_vector,
+            sentinel.azimuthal_transmission,
+            sentinel.central_effective_area,
+            sentinel.passband_fit_coefficients,
         ]
+        mock_select_mag_path.return_value = (sentinel.mag_path, "l2")
 
         swapi_science_file_download_path = f"{mission}_{instrument}_{data_level}_{SWAPI_L2_DESCRIPTOR}_{start_date}_{version}.cdf"
-        swapi_clock_angle_calibration_table_file_name = f"{mission}_{instrument}_{CLOCK_ANGLE_AND_FLOW_DEFLECTION_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf"
-        swapi_alpha_temp_density_calibration_file_name = f"{mission}_{instrument}_{ALPHA_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf"
-        swapi_proton_temp_and_density_calibration_file_name = f"{mission}_{instrument}_{PROTON_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf"
         swapi_efficiency_file_name = f"{mission}_{instrument}_{EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf"
         swapi_geometric_factor_calibration_file_name = f"{mission}_{instrument}_{GEOMETRIC_FACTOR_PUI_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf"
         swapi_instrument_response_lookup_table_collection = f"{mission}_{instrument}_{INSTRUMENT_RESPONSE_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf"
         swapi_density_of_neutral_helium_lookup = f"{mission}_{instrument}_{DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR}_{start_date}_{version}.cdf"
         swapi_hydrogen_inflow_filename = f"{mission}_{instrument}_{HYDROGEN_INFLOW_VECTOR_DESCRIPTOR}_{start_date}_{version}.cdf"
         swapi_helium_inflow_filename = f"{mission}_{instrument}_{HELIUM_INFLOW_VECTOR_DESCRIPTOR}_{start_date}_{version}.cdf"
+        swapi_azimuthal_transmission_filename = f"{mission}_{instrument}_{AZIMUTHAL_TRANSMISSION_DESCRIPTOR}_{start_date}_{version}.cdf"
+        swapi_central_effective_area_filename = f"{mission}_{instrument}_{CENTRAL_EFFECTIVE_AREA_DESCRIPTOR}_{start_date}_{version}.cdf"
+        swapi_passband_fit_coefficients_filename = f"{mission}_{instrument}_{PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR}_{start_date}_{version}.cdf"
 
         science_input = ScienceInput(swapi_science_file_download_path)
-        clock_angle_calibration_ancillary = AncillaryInput(swapi_clock_angle_calibration_table_file_name)
-        alpha_temp_density_calibration_ancillary = AncillaryInput(swapi_alpha_temp_density_calibration_file_name)
-        proton_temp_and_density_calibration_ancillary = AncillaryInput(
-            swapi_proton_temp_and_density_calibration_file_name)
         efficiency_ancillary = AncillaryInput(swapi_efficiency_file_name)
         geometric_factor_calibration_ancillary = AncillaryInput(swapi_geometric_factor_calibration_file_name)
         instrument_response_lookup_ancillary = AncillaryInput(swapi_instrument_response_lookup_table_collection)
         density_of_neutral_helium_ancillary = AncillaryInput(swapi_density_of_neutral_helium_lookup)
         hydrogen_inflow_ancillary = AncillaryInput(swapi_hydrogen_inflow_filename)
         helium_inflow_ancillary = AncillaryInput(swapi_helium_inflow_filename)
+        azimuthal_transmission_ancillary = AncillaryInput(swapi_azimuthal_transmission_filename)
+        central_effective_area_ancillary = AncillaryInput(swapi_central_effective_area_filename)
+        passband_fit_coefficients_ancillary = AncillaryInput(swapi_passband_fit_coefficients_filename)
 
         input_collection.add(
-            [science_input, clock_angle_calibration_ancillary, alpha_temp_density_calibration_ancillary,
-             proton_temp_and_density_calibration_ancillary, efficiency_ancillary,
+            [science_input, efficiency_ancillary,
              geometric_factor_calibration_ancillary,
              instrument_response_lookup_ancillary, density_of_neutral_helium_ancillary,
              hydrogen_inflow_ancillary,
              helium_inflow_ancillary,
+             azimuthal_transmission_ancillary,
+             central_effective_area_ancillary,
+             passband_fit_coefficients_ancillary,
              ])
 
         actual_swapi_l3_dependencies = SwapiL3ADependencies.fetch_dependencies(input_collection)
@@ -78,50 +80,86 @@ def test_fetch_dependencies(self, mock_download, mock_from_file_paths):
         science_data_dir = imap_data_access.config["DATA_DIR"] / 'imap' / 'swapi' / 'l2' / '2010' / '01'
         ancillary_data_dir = imap_data_access.config["DATA_DIR"] / 'imap' / 'ancillary' / 'swapi'
 
-        expected_download_science_path = science_data_dir / swapi_science_file_download_path
-        expected_download_ancillary_path1 = ancillary_data_dir / swapi_proton_temp_and_density_calibration_file_name
-        expected_download_ancillary_path2 = ancillary_data_dir / swapi_alpha_temp_density_calibration_file_name
-        expected_download_ancillary_path3 = ancillary_data_dir / swapi_clock_angle_calibration_table_file_name
-        expected_download_ancillary_path4 = ancillary_data_dir / swapi_efficiency_file_name
-        expected_download_ancillary_path5 = ancillary_data_dir / swapi_geometric_factor_calibration_file_name
-        expected_download_ancillary_path6 = ancillary_data_dir / swapi_instrument_response_lookup_table_collection
-        expected_download_ancillary_path7 = ancillary_data_dir / swapi_density_of_neutral_helium_lookup
-        expected_download_ancillary_path8 = ancillary_data_dir / swapi_hydrogen_inflow_filename
-        expected_download_ancillary_path9 = ancillary_data_dir / swapi_helium_inflow_filename
-
         mock_download.assert_has_calls([
-            call(expected_download_science_path),
-            call(expected_download_ancillary_path1),
-            call(expected_download_ancillary_path2),
-            call(expected_download_ancillary_path3),
-            call(expected_download_ancillary_path4),
-            call(expected_download_ancillary_path5),
-            call(expected_download_ancillary_path6),
-            call(expected_download_ancillary_path7),
-            call(expected_download_ancillary_path8),
-            call(expected_download_ancillary_path9),
+            call(science_data_dir / swapi_science_file_download_path),
+            call(ancillary_data_dir / swapi_efficiency_file_name),
+            call(ancillary_data_dir / swapi_geometric_factor_calibration_file_name),
+            call(ancillary_data_dir / swapi_instrument_response_lookup_table_collection),
+            call(ancillary_data_dir / swapi_density_of_neutral_helium_lookup),
+            call(ancillary_data_dir / swapi_hydrogen_inflow_filename),
+            call(ancillary_data_dir / swapi_helium_inflow_filename),
+            call(ancillary_data_dir / swapi_azimuthal_transmission_filename),
+            call(ancillary_data_dir / swapi_central_effective_area_filename),
+            call(ancillary_data_dir / swapi_passband_fit_coefficients_filename),
         ])
 
+        mock_select_mag_path.assert_called_once_with(input_collection, MAG_RTN_DESCRIPTOR)
+
         mock_from_file_paths.assert_called_with(
             sentinel.swapi_l2_data,
-            sentinel.proton_density_and_temperature_calibration_file,
-            sentinel.alpha_density_and_temperature_calibration_file,
-            sentinel.clock_and_deflection_file,
             sentinel.efficiency_file,
             sentinel.geometric_factor_calibration_table,
             sentinel.instrument_response_table,
             sentinel.neutral_helium_table,
             sentinel.hydrogen_vector,
             sentinel.helium_vector,
+            sentinel.azimuthal_transmission,
+            sentinel.central_effective_area,
+            sentinel.passband_fit_coefficients,
+            sentinel.mag_path,
+            False,
         )
 
         self.assertEqual(mock_from_file_paths.return_value, actual_swapi_l3_dependencies)
 
+    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.select_mag_path")
+    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.SwapiL3ADependencies.from_file_paths")
+    @patch("imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.download")
+    def test_fetch_dependencies_with_l1d_mag(self, mock_download, mock_from_file_paths, mock_select_mag_path):
+        input_collection = ProcessingInputCollection()
+
+        mock_download.side_effect = [
+            sentinel.swapi_l2_data,
+            sentinel.efficiency_file,
+            sentinel.geometric_factor_calibration_table,
+            sentinel.instrument_response_table,
+            sentinel.neutral_helium_table,
+            sentinel.hydrogen_vector,
+            sentinel.helium_vector,
+            sentinel.azimuthal_transmission,
+            sentinel.central_effective_area,
+            sentinel.passband_fit_coefficients,
+        ]
+        mock_select_mag_path.return_value = (sentinel.mag_path, "l1d")
+
+        start_date = '20100105'
+        version = 'v010'
+        descriptors = [
+            ("imap_swapi_l2", SWAPI_L2_DESCRIPTOR, ScienceInput),
+            ("imap_swapi", EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR, AncillaryInput),
+            ("imap_swapi", GEOMETRIC_FACTOR_PUI_LOOKUP_TABLE_DESCRIPTOR, AncillaryInput),
+            ("imap_swapi", INSTRUMENT_RESPONSE_LOOKUP_TABLE_DESCRIPTOR, AncillaryInput),
+            ("imap_swapi", DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR, AncillaryInput),
+            ("imap_swapi", HYDROGEN_INFLOW_VECTOR_DESCRIPTOR, AncillaryInput),
+            ("imap_swapi", HELIUM_INFLOW_VECTOR_DESCRIPTOR, AncillaryInput),
+            ("imap_swapi", AZIMUTHAL_TRANSMISSION_DESCRIPTOR, AncillaryInput),
+            ("imap_swapi", CENTRAL_EFFECTIVE_AREA_DESCRIPTOR, AncillaryInput),
+            ("imap_swapi", PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR, AncillaryInput),
+        ]
+        for prefix, desc, cls in descriptors:
+            input_collection.add([cls(f"{prefix}_{desc}_{start_date}_{version}.cdf")])
+
+        SwapiL3ADependencies.fetch_dependencies(input_collection)
+
+        _, kwargs = mock_from_file_paths.call_args
+        args = mock_from_file_paths.call_args.args
+        self.assertEqual(sentinel.mag_path, args[10])
+        self.assertEqual(True, args[11])
+
+    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.read_mag_rtn_data')
+    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.SwapiResponse.from_files')
     @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.CDF')
     @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.InflowVector.from_file')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.ProtonTemperatureAndDensityCalibrationTable.from_file')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.AlphaTemperatureDensityCalibrationTable.from_file')
-    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.ClockAngleCalibrationTable.from_file')
     @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.EfficiencyCalibrationTable')
     @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.GeometricFactorCalibrationTable.from_file')
     @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.InstrumentResponseLookupTableCollection.from_file')
@@ -129,81 +167,99 @@ def test_fetch_dependencies(self, mock_download, mock_from_file_paths):
     @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.read_l2_swapi_data')
     def test_from_file_paths(self, mock_read_l2_swapi, mock_neutral_helium_from_file,
                              mock_instrument_from_file, mock_geometric_from_file, mock_efficiency_lookup_class,
-                             mock_clock_angle_from_file, mock_alpha_temp_from_file,
-                             mock_proton_temp_from_file, mock_inflow_vector_from_file, mock_CDF):
-        start_date = '20100105'
-        mission = 'imap'
-        instrument = 'swapi'
-        data_level = 'l2'
-        version = 'v010'
-
-        swapi_science_file_download_path = Path(
-            f"{mission}_{instrument}_{data_level}_{SWAPI_L2_DESCRIPTOR}_{start_date}_{version}.cdf")
-        swapi_clock_angle_calibration_table_file_name = Path(
-            f"{mission}_{instrument}_{CLOCK_ANGLE_AND_FLOW_DEFLECTION_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf")
-        swapi_alpha_temp_density_calibration_file_name = Path(
-            f"{mission}_{instrument}_{ALPHA_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf")
-        swapi_proton_temp_and_density_calibration_file_name = Path(
-            f"{mission}_{instrument}_{PROTON_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf")
-        swapi_efficiency_calibration_file_name = Path(
-            f"{mission}_{instrument}_{EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf")
-        swapi_geometric_factor_calibration_file_name = Path(
-            f"{mission}_{instrument}_{GEOMETRIC_FACTOR_PUI_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf")
-        swapi_instrument_response_lookup_table_collection = Path(
-            f"{mission}_{instrument}_{INSTRUMENT_RESPONSE_LOOKUP_TABLE_DESCRIPTOR}_{start_date}_{version}.cdf")
-        swapi_density_of_neutral_helium_lookup = Path(
-            f"{mission}_{instrument}_{DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR}_{start_date}_{version}.cdf")
-        swapi_hydrogen_vector_path = Path(
-            f"{mission}_{instrument}_{HYDROGEN_INFLOW_VECTOR_DESCRIPTOR}_{start_date}_{version}.dat")
-        swapi_helium_vector_path = Path(
-            f"{mission}_{instrument}_{HELIUM_INFLOW_VECTOR_DESCRIPTOR}_{start_date}_{version}.dat")
+                             mock_inflow_vector_from_file, mock_CDF,
+                             mock_swapi_response_from_files, mock_read_mag_rtn_data):
+        science_path = Path("imap_swapi_l2_sci_20100105_v010.cdf")
+        efficiency_path = Path("imap_swapi_efficiency_20100105_v010.cdf")
+        geometric_path = Path("imap_swapi_geometric_20100105_v010.cdf")
+        instrument_response_path = Path("imap_swapi_response_20100105_v010.cdf")
+        neutral_helium_path = Path("imap_swapi_neutral_he_20100105_v010.cdf")
+        hydrogen_vector_path = Path("imap_swapi_h_inflow_20100105_v010.dat")
+        helium_vector_path = Path("imap_swapi_he_inflow_20100105_v010.dat")
+        azimuthal_transmission_path = Path("imap_swapi_azimuthal_transmission_20100105_v010.cdf")
+        central_effective_area_path = Path("imap_swapi_central_effective_area_20100105_v010.cdf")
+        passband_fit_coefficients_path = Path("imap_swapi_passband_fit_coefficients_20100105_v010.cdf")
+        mag_path = Path("imap_mag_l2_norm-rtn_20100105_v010.cdf")
 
         mock_read_l2_swapi.return_value = sentinel.swapi_l2_data
-        mock_proton_temp_from_file.return_value = sentinel.proton_temp_data
-        mock_alpha_temp_from_file.return_value = sentinel.alpha_temp_data
-        mock_clock_angle_from_file.return_value = sentinel.clock_angle_data
         mock_efficiency_lookup_class.return_value = sentinel.efficiency_lookup
         mock_geometric_from_file.return_value = sentinel.geometric_data
         mock_instrument_from_file.return_value = sentinel.instrument_data
         mock_neutral_helium_from_file.return_value = sentinel.neutral_helium_data
         mock_inflow_vector_from_file.side_effect = [sentinel.hydrogen_vector, sentinel.helium_vector]
+        mock_swapi_response_from_files.return_value = sentinel.swapi_response
+        mock_read_mag_rtn_data.return_value = sentinel.mag_data
 
-        expected_dependencies = SwapiL3ADependencies(sentinel.swapi_l2_data,
-                                                     sentinel.proton_temp_data,
-                                                     sentinel.alpha_temp_data,
-                                                     sentinel.clock_angle_data,
-                                                     sentinel.efficiency_lookup,
-                                                     sentinel.geometric_data,
-                                                     sentinel.instrument_data,
-                                                     sentinel.neutral_helium_data,
-                                                     sentinel.hydrogen_vector,
-                                                     sentinel.helium_vector,
-                                                     )
+        expected_dependencies = SwapiL3ADependencies(
+            data=sentinel.swapi_l2_data,
+            efficiency_calibration_table=sentinel.efficiency_lookup,
+            geometric_factor_calibration_table=sentinel.geometric_data,
+            instrument_response_calibration_table=sentinel.instrument_data,
+            density_of_neutral_helium_calibration_table=sentinel.neutral_helium_data,
+            hydrogen_inflow_vector=sentinel.hydrogen_vector,
+            helium_inflow_vector=sentinel.helium_vector,
+            swapi_response=sentinel.swapi_response,
+            mag_data=sentinel.mag_data,
+            mag_is_preliminary=True,
+        )
 
         actual_dependencies = SwapiL3ADependencies.from_file_paths(
-            swapi_science_file_download_path,
-            swapi_proton_temp_and_density_calibration_file_name,
-            swapi_alpha_temp_density_calibration_file_name,
-            swapi_clock_angle_calibration_table_file_name,
-            swapi_efficiency_calibration_file_name,
-            swapi_geometric_factor_calibration_file_name,
-            swapi_instrument_response_lookup_table_collection,
-            swapi_density_of_neutral_helium_lookup,
-            swapi_hydrogen_vector_path,
-            swapi_helium_vector_path,
+            science_path,
+            efficiency_path,
+            geometric_path,
+            instrument_response_path,
+            neutral_helium_path,
+            hydrogen_vector_path,
+            helium_vector_path,
+            azimuthal_transmission_path,
+            central_effective_area_path,
+            passband_fit_coefficients_path,
+            mag_path,
+            True,
         )
 
-        mock_CDF.assert_called_once_with(str(swapi_science_file_download_path))
-
+        mock_CDF.assert_called_once_with(str(science_path))
         mock_read_l2_swapi.assert_called_once_with(mock_CDF.return_value)
-        mock_proton_temp_from_file.assert_called_once_with(swapi_proton_temp_and_density_calibration_file_name)
-        mock_alpha_temp_from_file.assert_called_once_with(swapi_alpha_temp_density_calibration_file_name)
-        mock_clock_angle_from_file.assert_called_once_with(swapi_clock_angle_calibration_table_file_name)
-        mock_efficiency_lookup_class.assert_called_once_with(swapi_efficiency_calibration_file_name)
-        mock_geometric_from_file.assert_called_once_with(swapi_geometric_factor_calibration_file_name)
-        mock_instrument_from_file.assert_called_once_with(swapi_instrument_response_lookup_table_collection)
-        mock_neutral_helium_from_file.assert_called_once_with(swapi_density_of_neutral_helium_lookup)
+        mock_efficiency_lookup_class.assert_called_once_with(efficiency_path)
+        mock_geometric_from_file.assert_called_once_with(geometric_path)
+        mock_instrument_from_file.assert_called_once_with(instrument_response_path)
+        mock_neutral_helium_from_file.assert_called_once_with(neutral_helium_path)
         mock_inflow_vector_from_file.assert_has_calls(
-            [call(swapi_hydrogen_vector_path), call(swapi_helium_vector_path)])
+            [call(hydrogen_vector_path), call(helium_vector_path)])
+        mock_swapi_response_from_files.assert_called_once_with(
+            azimuthal_transmission_path, central_effective_area_path, passband_fit_coefficients_path)
+        mock_read_mag_rtn_data.assert_called_once_with(mag_path)
 
         self.assertEqual(expected_dependencies, actual_dependencies)
+
+    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.read_mag_rtn_data')
+    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.SwapiResponse.from_files')
+    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.CDF')
+    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.InflowVector.from_file')
+    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.EfficiencyCalibrationTable')
+    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.GeometricFactorCalibrationTable.from_file')
+    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.InstrumentResponseLookupTableCollection.from_file')
+    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.DensityOfNeutralHeliumLookupTable.from_file')
+    @patch('imap_l3_processing.swapi.l3a.swapi_l3a_dependencies.read_l2_swapi_data')
+    def test_from_file_paths_without_mag(self, mock_read_l2_swapi, mock_neutral_helium_from_file,
+                                         mock_instrument_from_file, mock_geometric_from_file,
+                                         mock_efficiency_lookup_class, mock_inflow_vector_from_file, mock_CDF,
+                                         mock_swapi_response_from_files, mock_read_mag_rtn_data):
+        mock_inflow_vector_from_file.side_effect = [sentinel.hydrogen_vector, sentinel.helium_vector]
+
+        actual = SwapiL3ADependencies.from_file_paths(
+            Path("science.cdf"),
+            Path("efficiency.cdf"),
+            Path("geometric.cdf"),
+            Path("instrument_response.cdf"),
+            Path("neutral_helium.cdf"),
+            Path("h_inflow.dat"),
+            Path("he_inflow.dat"),
+            Path("azimuthal_transmission.cdf"),
+            Path("central_effective_area.cdf"),
+            Path("passband_fit_coefficients.cdf"),
+        )
+
+        self.assertIsNone(actual.mag_data)
+        self.assertFalse(actual.mag_is_preliminary)
+        mock_read_mag_rtn_data.assert_not_called()
diff --git a/tests/swapi/l3a/test_utils.py b/tests/swapi/l3a/test_utils.py
index 86c75a28f..542fc7e9c 100644
--- a/tests/swapi/l3a/test_utils.py
+++ b/tests/swapi/l3a/test_utils.py
@@ -1,20 +1,36 @@
 import os
+from datetime import datetime
 from pathlib import Path
 from unittest import TestCase
 
 import numpy as np
+import spacepy.pycdf
 from spacepy.pycdf import CDF
+from uncertainties import UFloat, ufloat
 
+from imap_l3_processing.constants import (
+    METERS_PER_KILOMETER,
+    PROTON_CHARGE_COULOMBS,
+    PROTON_MASS_KG,
+)
 from imap_l3_processing.swapi.l3a.models import SwapiL2Data
-from imap_l3_processing.swapi.l3a.utils import chunk_l2_data, read_l2_swapi_data
+from imap_l3_processing.swapi.l3a.utils import (
+    calculate_sw_speed,
+    chunk_l2_data,
+    get_spacecraft_velocity_rtn,
+    get_swapi_geometry,
+    read_l2_swapi_data,
+    read_mag_rtn_data,
+    rotate_rtn_to_dps,
+)
+from tests.spice_test_case import SpiceTestCase
 
 
-class TestUtils(TestCase):
-    def tearDown(self) -> None:
-        if os.path.exists('temp_cdf.cdf'):
-            os.remove('temp_cdf.cdf')
+class TestChunkL2Data(TestCase):
+    """Tests for `chunk_l2_data`."""
 
     def test_chunk_l2_data(self):
+        """chunk_l2_data splits a 4-sweep L2 dataset into two 2-sweep chunks, copying epoch, energy, count rate, and uncertainty into each chunk."""
         epoch = np.array([0, 1, 2, 3])
         energy = np.array([[15000, 16000, 17000, 18000, 19000],
                            [25000, 26000, 27000, 28000, 29000],
@@ -54,7 +70,29 @@ def test_chunk_l2_data(self):
         np.testing.assert_array_equal(expected_count_rate_uncertainty_chunk_2,
                                       second_chunk.coincidence_count_rate_uncertainty)
 
+    def test_chunk_l2_data_partial_trailing_chunk_is_dropped(self):
+        """When the sweep count is not a multiple of the chunk size, the trailing partial chunk is dropped rather than yielded short."""
+        rates = np.arange(7 * 4, dtype=float).reshape(7, 4)
+        data = SwapiL2Data(
+            sci_start_time=np.arange(7, dtype=np.int64),
+            energy=rates.copy(),
+            coincidence_count_rate=rates.copy(),
+            coincidence_count_rate_uncertainty=rates.copy(),
+        )
+        chunks = list(chunk_l2_data(data, 5))
+        self.assertEqual(len(chunks), 1)
+        np.testing.assert_array_equal(chunks[0].sci_start_time, np.arange(5))
+
+
+class TestReadL2SwapiData(TestCase):
+    """Tests for `read_l2_swapi_data`."""
+
+    def tearDown(self) -> None:
+        if os.path.exists('temp_cdf.cdf'):
+            os.remove('temp_cdf.cdf')
+
     def test_reading_l2_data_into_model(self):
+        """read_l2_swapi_data parses a CDF into SwapiL2Data, decoding the start time to TT2000 and replacing each variable's FILLVAL entries with NaN."""
         path = Path('temp_cdf.cdf')
         if path.exists():
             os.remove(path)
@@ -80,3 +118,145 @@ def test_reading_l2_data_into_model(self):
         np.testing.assert_array_equal(np.array([5, 6, 7, np.nan]), actual_swapi_l2_data.coincidence_count_rate)
         np.testing.assert_array_equal(np.array([2, 2, np.nan, 2, 2, 2, 2, 2]),
                                       actual_swapi_l2_data.coincidence_count_rate_uncertainty)
+
+
+class TestCalculateSwSpeed(TestCase):
+    """Tests for `calculate_sw_speed`."""
+
+    def test_2d_array_matches_analytic_formula_per_element(self):
+        """calculate_sw_speed on a 2D energy array matches the analytic v = sqrt(2qE/m) (in km/s) element-by-element."""
+        E = np.array([[1.0e-16, 2.0e-16], [4.0e-16, 8.0e-16]])
+        expected = (
+            np.sqrt(2 * E * PROTON_CHARGE_COULOMBS / PROTON_MASS_KG)
+            / METERS_PER_KILOMETER
+        )
+        result = calculate_sw_speed(PROTON_MASS_KG, PROTON_CHARGE_COULOMBS, E)
+        np.testing.assert_allclose(result, expected)
+
+    def test_2d_array_input_preserves_shape(self):
+        """calculate_sw_speed preserves the input array shape (no flattening or broadcasting collapse)."""
+        E = np.array([[1.0e-16, 2.0e-16], [4.0e-16, 8.0e-16]])
+        result = calculate_sw_speed(PROTON_MASS_KG, PROTON_CHARGE_COULOMBS, E)
+        self.assertEqual(result.shape, E.shape)
+
+    def test_empty_array_input_returns_empty_array(self):
+        """calculate_sw_speed returns an empty array when given one, without errors."""
+        result = calculate_sw_speed(
+            PROTON_MASS_KG, PROTON_CHARGE_COULOMBS, np.array([])
+        )
+        self.assertEqual(result.size, 0)
+
+    def test_ufloat_scalar_propagates_uncertainty(self):
+        """A scalar ufloat energy returns a ufloat speed with uncertainty σ_v = v · σ_E / (2E) per first-order error propagation."""
+        E = ufloat(1.0e-16, 1.0e-18)
+        result = calculate_sw_speed(PROTON_MASS_KG, PROTON_CHARGE_COULOMBS, E)
+        self.assertIsInstance(result, UFloat)
+        # σ_v = v · σ_E / (2 E)
+        expected_nom = (
+            np.sqrt(2 * E.nominal_value * PROTON_CHARGE_COULOMBS / PROTON_MASS_KG)
+            / METERS_PER_KILOMETER
+        )
+        expected_sigma = expected_nom * E.std_dev / (2 * E.nominal_value)
+        self.assertAlmostEqual(result.nominal_value, expected_nom)
+        self.assertAlmostEqual(result.std_dev, expected_sigma)
+
+    def test_ufloat_array_input_propagates_uncertainty_per_element(self):
+        """A numpy array of UFloat energies returns per-element UFloat speeds with each element's own σ_E correctly propagated."""
+        E_values = np.array([ufloat(1.0e-16, 1.0e-18), ufloat(4.0e-16, 2.0e-18)])
+        result = calculate_sw_speed(PROTON_MASS_KG, PROTON_CHARGE_COULOMBS, E_values)
+        self.assertEqual(result.shape, E_values.shape)
+        for r, E in zip(result, E_values):
+            self.assertIsInstance(r, UFloat)
+            expected_nom = (
+                np.sqrt(2 * E.nominal_value * PROTON_CHARGE_COULOMBS / PROTON_MASS_KG)
+                / METERS_PER_KILOMETER
+            )
+            self.assertAlmostEqual(r.nominal_value, expected_nom)
+            self.assertAlmostEqual(
+                r.std_dev, expected_nom * E.std_dev / (2 * E.nominal_value)
+            )
+
+
+class TestReadMagRtnData(TestCase):
+    """Tests for `read_mag_rtn_data`."""
+
+    def setUp(self) -> None:
+        self.cdf_path = Path('temp_mag_cdf.cdf')
+        if self.cdf_path.exists():
+            os.remove(self.cdf_path)
+
+    def tearDown(self) -> None:
+        if self.cdf_path.exists():
+            os.remove(self.cdf_path)
+
+    def test_reads_b_rtn_and_epoch_into_mag_data(self):
+        """read_mag_rtn_data converts CDF epochs to TT2000 and keeps only the leading three vector components of b_rtn (dropping the magnitude column)."""
+        epochs = np.array([datetime(2026, 1, 1, 0, 0, 0),
+                           datetime(2026, 1, 1, 0, 0, 1)])
+        b_rtn = np.array(
+            [[1.0, 2.0, 3.0, 0.0],
+             [4.0, 5.0, 6.0, 0.0]],
+        )
+        cdf = CDF(str(self.cdf_path.with_suffix("")), '')
+        cdf["epoch"] = epochs
+        cdf["b_rtn"] = b_rtn
+        cdf["b_rtn"].attrs["FILLVAL"] = -1e31
+        cdf.close()
+
+        result = read_mag_rtn_data(self.cdf_path)
+
+        expected_epoch_tt2000 = spacepy.pycdf.lib.v_datetime_to_tt2000(epochs)
+        np.testing.assert_array_equal(result.epoch, expected_epoch_tt2000)
+        np.testing.assert_array_equal(
+            result.mag_data, np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+        )
+
+
+class TestSwapiSpiceHelpers(SpiceTestCase):
+    """Tests for the SPICE helpers `get_swapi_geometry`, `rotate_rtn_to_dps`, and `get_spacecraft_velocity_rtn`."""
+
+    # 2025-06-06 12:00 UTC — inside the IMAP SPK and attitude coverage windows
+    # used by the shipped `spice_kernels/` set.
+    _EPOCH_TT2000_NS = spacepy.pycdf.lib.datetime_to_tt2000(
+        datetime(2025, 6, 6, 12, 0, 0)
+    )
+
+    def test_get_swapi_geometry_returns_orthonormal_rotation_per_time(self):
+        """get_swapi_geometry returns one proper-orthogonal (right-handed) rotation matrix per input time across a 30 s window."""
+        # Three TT2000 ns samples spanning ~30 s.
+        # IMAP's spin period is 15 s,
+        # so these should produce distinct rotations.
+        times = self._EPOCH_TT2000_NS + np.array([0, 10_000_000_000, 20_000_000_000])
+
+        rotation_matrices = get_swapi_geometry(times)
+
+        self.assertEqual(rotation_matrices.shape, (3, 3, 3))
+        for matrix in rotation_matrices:
+            np.testing.assert_allclose(matrix @ matrix.T, np.eye(3), atol=1e-10)
+            self.assertAlmostEqual(float(np.linalg.det(matrix)), 1.0, places=10)
+
+    def test_rotate_rtn_to_dps_preserves_vector_magnitude(self):
+        """rotate_rtn_to_dps applies a pure rotation, so the magnitude of the output vector equals the input magnitude."""
+        vector_rtn = np.array([100.0, -50.0, 25.0])
+
+        rotated = rotate_rtn_to_dps(vector_rtn, self._EPOCH_TT2000_NS)
+
+        self.assertEqual(rotated.shape, (3,))
+        self.assertAlmostEqual(
+            float(np.linalg.norm(rotated)),
+            float(np.linalg.norm(vector_rtn)),
+            places=8,
+        )
+
+    def test_get_spacecraft_velocity_rtn_returns_finite_orbital_velocity(self):
+        """get_spacecraft_velocity_rtn returns a finite 3-vector with magnitude in the 10–60 km/s band, ruling out unit-conversion mistakes for IMAP near L1."""
+        velocity_rtn = get_spacecraft_velocity_rtn(self._EPOCH_TT2000_NS)
+
+        self.assertEqual(velocity_rtn.shape, (3,))
+        self.assertTrue(np.all(np.isfinite(velocity_rtn)))
+        # IMAP sits near L1; its barycentric speed is dominated by Earth's
+        # ~30 km/s orbital motion. A loose bound rules out unit-conversion
+        # mistakes (m/s vs km/s) without pinning a frame-specific value.
+        speed = float(np.linalg.norm(velocity_rtn))
+        self.assertGreater(speed, 10.0)
+        self.assertLess(speed, 60.0)
diff --git a/tests/swapi/response/__init__.py b/tests/swapi/response/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/swapi/response/test_azimuthal_transmission.py b/tests/swapi/response/test_azimuthal_transmission.py
new file mode 100644
index 000000000..58d177fbf
--- /dev/null
+++ b/tests/swapi/response/test_azimuthal_transmission.py
@@ -0,0 +1,166 @@
+import unittest
+
+import numpy as np
+import pandas as pd
+
+from imap_l3_processing.swapi.response.azimuthal_transmission import (
+    AzimuthalTransmissionGrid,
+    interpolate_azimuthal_transmission,
+)
+from imap_l3_processing.swapi.response.swapi_response import SwapiResponse
+from tests.test_helpers import get_test_instrument_team_data_path
+
+_TRANSMISSION_CSV = get_test_instrument_team_data_path(
+    "swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv"
+)
+_SPACING_DEG = SwapiResponse.AZIMUTHAL_TRANSMISSION_SPACING_DEG
+
+
+def _load_real_grid() -> AzimuthalTransmissionGrid:
+    # Build the grid the same way `SwapiResponse.from_files` does.
+    df = pd.read_csv(_TRANSMISSION_CSV)
+    values = df["transmission"].fillna(0).values.astype(float)
+    return AzimuthalTransmissionGrid(values=values, spacing=_SPACING_DEG)
+
+
+class TestInterpolateAzimuthalTransmission(unittest.TestCase):
+    """
+    `interpolate_azimuthal_transmission` linearly interpolates the azimuthal
+    transmission curve `T(|az|)` with two cases of special handling:
+
+    (a) the input azimuth is wrapped into (-180°, 180°] before lookup, and
+    (b) the lookup uses `|az|`, so the curve is implicitly mirrored at zero.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        cls.grid = _load_real_grid()
+        cls.values = cls.grid.values
+        cls.n = len(cls.values)
+        cls.last_az = _SPACING_DEG * (cls.n - 1)
+
+    def test_at_grid_point_returns_csv_value(self):
+        """Exact-index lookups: interpolation weights collapse to 1, so the
+        result must equal the CSV cell at that index. Sample indices span
+        the SG attenuated region, the SG/OA shoulder, the OA plateau, and
+        the zero-padded tail."""
+        for idx in [0, 10, 199, 299, 500, 1000, self.n - 1]:
+            with self.subTest(idx=idx):
+                az = idx * _SPACING_DEG
+                got = interpolate_azimuthal_transmission(self.grid, az)
+                self.assertAlmostEqual(got, float(self.values[idx]))
+
+    def test_linear_interpolation_halfway_between_grid_points(self):
+        """Halfway between two adjacent CSV rows must equal their average.
+        Pick a pair on the SG→OA rising shoulder so the two endpoints
+        differ meaningfully (rather than both sitting on the SG floor)."""
+        idx = 250  # az ≈ 25°, mid-shoulder
+        az_lower = idx * _SPACING_DEG
+        midpoint_az = az_lower + 0.5 * _SPACING_DEG
+        expected = 0.5 * (self.values[idx] + self.values[idx + 1])
+        got = interpolate_azimuthal_transmission(self.grid, midpoint_az)
+        self.assertAlmostEqual(got, float(expected))
+
+    def test_linear_interpolation_off_center_between_grid_points(self):
+        """Off-center fractions exercise the interpolation weights, not just
+        the symmetric midpoint case: at fraction f between idx and idx+1, the
+        result must be `(1-f)·v[idx] + f·v[idx+1]`. Pick the same SG→OA
+        shoulder pair so the endpoints differ meaningfully."""
+        idx = 250  # az ≈ 25°, mid-shoulder
+        az_lower = idx * _SPACING_DEG
+        for fraction in [0.25, 0.7, 0.9]:
+            with self.subTest(fraction=fraction):
+                az = az_lower + fraction * _SPACING_DEG
+                expected = (1 - fraction) * self.values[idx] + fraction * self.values[idx + 1]
+                got = interpolate_azimuthal_transmission(self.grid, az)
+                self.assertAlmostEqual(got, float(expected))
+
+    def test_symmetric_about_zero(self):
+        """The lookup uses |az|, so T(-x) must equal T(+x) for any x."""
+        for az in [0.37, 12.5, 29.95, 88.123]:
+            with self.subTest(az=az):
+                positive = interpolate_azimuthal_transmission(self.grid, az)
+                negative = interpolate_azimuthal_transmission(self.grid, -az)
+                self.assertAlmostEqual(positive, negative)
+
+    def test_clamps_at_and_past_the_last_index(self):
+        """At and past the last stored index, both bracketing neighbors are
+        clamped to the array end so the result equals the last table value.
+        Covers three sub-cases that share the same expected outcome:
+          - exactly on the last index (i_lower = n-1, i_upper = n)
+          - just past the last index (both indices need clamping)
+          - near the upper edge of the wrap interval
+        """
+        last_value = float(self.values[-1])
+        for label, az in [
+            ("exactly on last index", self.last_az),
+            ("just past last index", self.last_az + 0.07),
+            ("near upper wrap edge", 179.95),
+        ]:
+            with self.subTest(case=label, az=az):
+                got = interpolate_azimuthal_transmission(self.grid, az)
+                self.assertEqual(got, last_value)
+
+    def test_wraps_arguments_into_canonical_180_interval(self):
+        """The wrap formula `(az + 180) % 360 - 180` maps any real azimuth into
+        (-180°, 180°]. Some particular cases:
+          - 359° wraps to -1° (|az| = 1°)
+          - exact ±180° both wrap to 180°
+          - -181° wraps to +179° (|az| = 179°)
+          - multi-period inputs (±541°) wrap into the canonical interval
+            (541 mod 360 = 181 → wraps to -179° → |az| = 179°)
+        """
+        idx_at_1deg = int(round(1.0 / _SPACING_DEG))
+        expected_at_1deg = float(self.values[idx_at_1deg])
+        last_value = float(self.values[-1])
+        idx_at_179deg = int(round(179.0 / _SPACING_DEG))
+        expected_at_179deg = float(self.values[idx_at_179deg])
+
+        for label, az, expected in [
+            ("almost full revolution", 359.0, expected_at_1deg),
+            ("exact +180", 180.0, last_value),
+            ("exact -180", -180.0, last_value),
+            ("just past -180", -181.0, expected_at_179deg),
+            ("multi-period positive", 541.0, expected_at_179deg),
+            ("multi-period negative", -541.0, expected_at_179deg),
+        ]:
+            with self.subTest(case=label, az=az):
+                got = interpolate_azimuthal_transmission(self.grid, az)
+                self.assertAlmostEqual(got, expected)
+
+
+class TestInterpolateAzimuthalTransmissionWithShorterGrid(unittest.TestCase):
+    """`interpolate_azimuthal_transmission` against a synthetic grid that does
+    not span the full 0-180 deg range, used to exercise the `i_lower >= n`
+    clamp branch that the production-sized grid cannot reach."""
+
+    def test_clamps_lower_index_when_past_grid_extent(self):
+        """When the floor of `|az|/spacing` lands past the last array index,
+        both bracketing indices clamp to `n-1`. With a tiny synthetic grid
+        whose extent (3°) is well below the canonical 180° wrap interval,
+        a wrapped azimuth of 10° drives `i_lower` past the end; the lookup
+        completes without raising and reads only the final stored value."""
+        grid = AzimuthalTransmissionGrid(
+            values=np.array([0.1, 0.2, 0.3, 0.4]),
+            spacing=1.0,
+        )
+        # |az| = 10° / spacing 1° = 10, well past last index 3 (n=4).
+        got = interpolate_azimuthal_transmission(grid, 10.0)
+        self.assertTrue(np.isfinite(got))
+
+
+class TestRealGridShape(unittest.TestCase):
+    """Sanity-check the loaded CSV layout — the interpolator tests above
+    assume 0.1° spacing and a fixed table length."""
+
+    def test_csv_layout_matches_production_spacing(self):
+        grid = _load_real_grid()
+        self.assertEqual(grid.spacing, 0.1)
+        # CSV spans 0° to 180° inclusive at 0.1° → 1801 rows.
+        self.assertEqual(len(grid.values), 1801)
+        # The SG region is attenuated by ≈1/1000 at az=0.
+        self.assertAlmostEqual(float(grid.values[0]), 1e-3)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/response/test_deadtime.py b/tests/swapi/response/test_deadtime.py
new file mode 100644
index 000000000..210c1e8c8
--- /dev/null
+++ b/tests/swapi/response/test_deadtime.py
@@ -0,0 +1,45 @@
+import unittest
+
+import numpy as np
+
+from imap_l3_processing.swapi.response.deadtime import (
+    SWAPI_DEADTIME_S,
+    deadtime_factor,
+)
+
+
+class TestDeadtimeFactor(unittest.TestCase):
+    def test_zero_rate_produces_unit_factor(self):
+        self.assertEqual(deadtime_factor(0.0), 1.0)
+
+    def test_factor_is_bounded_in_unit_interval(self):
+        # For physical (non-negative) rates the factor must lie in (0, 1].
+        for r in [0.0, 1e3, 1e6, 1e9, 1e12]:
+            f = deadtime_factor(float(r))
+            self.assertGreater(f, 0.0)
+            self.assertLessEqual(f, 1.0)
+
+    def test_factor_is_monotonically_decreasing(self):
+        # Higher count rates lose more events to deadtime — the factor is a
+        # strictly decreasing function of rate. Sample across six decades to
+        # catch any sign or denominator typo.
+        rates = np.logspace(0, 9, 10)
+        factors = np.array([deadtime_factor(float(r)) for r in rates])
+        self.assertTrue(np.all(np.diff(factors) < 0))
+
+    def test_226_hz_loss_at_35000_hz_measured_rate(self):
+        # SWAPI calibration documents that a 35.000 kHz *measured* rate
+        # corresponds to a 35.226 kHz *true* rate (i.e. ~226 Hz lost to
+        # deadtime). Source: SWAPI deadtime calibration documentation.
+        true_rate_hz = 35_226
+        measured_rate_hz = true_rate_hz * deadtime_factor(true_rate_hz)
+        np.testing.assert_allclose(measured_rate_hz, 35_000, rtol=1e-4)
+
+    def test_factor_is_half_when_rate_equals_one_over_tau(self):
+        # 1 / (1 + tau · (1/tau)) = 1/2.
+        rate = 1.0 / SWAPI_DEADTIME_S
+        np.testing.assert_allclose(deadtime_factor(rate), 0.5, rtol=1e-12)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/response/test_passband_grid.py b/tests/swapi/response/test_passband_grid.py
new file mode 100644
index 000000000..32a773757
--- /dev/null
+++ b/tests/swapi/response/test_passband_grid.py
@@ -0,0 +1,333 @@
+import unittest
+
+import numpy as np
+import pandas as pd
+
+from imap_l3_processing.swapi.response.passband_grid import (
+    _PASSBAND_BOUNDARY_THRESHOLD,
+    _TARGET_ELEVATIONS,
+    _TARGET_SPEED_RATIOS,
+    build_passband_grid,
+    interpolate_passband,
+    speed_ratio_range_at_elevation,
+)
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+
+
+def _gaussian_values_df(
+    peak_elevation: float = 0.0,
+    peak_speed_ratio: float = 1.0,
+    sigma_el: float = 1.0,
+    sigma_er: float = 0.1,
+):
+    """Synthetic per-region passband DataFrame indexed by `(energy_ratio, elevation)`,
+    in the format `build_passband_grid` consumes. A single Gaussian bump centered
+    at `(peak_elevation, peak_speed_ratio)` lets callers dial peak location and
+    width to exercise specific branches. The sampling grid is deliberately wider
+    than the target elevation range and at a different speed-ratio resolution so
+    that the resampling step in `build_passband_grid` is non-trivial."""
+    # Wider elevation range than target (-15..15) with extra rows on either side
+    # that the resampler must drop. Elevation step matches target so every
+    # target row finds an exact match in the DF index (the source uses `elev in
+    # pivot.index`, an equality lookup).
+    elevations = np.arange(-17.0, 17.0 + 0.5, 0.5)
+    # Coarser, slightly wider speed-ratio grid than target — the resampler
+    # interpolates between input nodes onto the denser target axis.
+    speed_ratios = np.linspace(0.85, 1.15, 41)
+    peak_er = SWAPI_K_FACTOR * peak_speed_ratio**2
+    rows = [
+        {
+            "elevation": float(elev),
+            "energy_ratio": float(SWAPI_K_FACTOR * sr**2),
+            "value": float(
+                # round so that there are true zeros like the real SIMION-derived passband
+                np.exp(
+                    -((elev - peak_elevation) ** 2) / (2.0 * sigma_el**2)
+                    - ((SWAPI_K_FACTOR * sr**2 - peak_er) ** 2)
+                    / (2.0 * sigma_er**2)
+                )
+            ),
+        }
+        for elev in elevations
+        for sr in speed_ratios
+    ]
+    return pd.DataFrame(rows).set_index(["energy_ratio", "elevation"])
+
+
+def _row_index_for_elevation(grid, elevation: float) -> int:
+    """Index into `grid.values` for the row at the given target elevation."""
+    return int(round((elevation - grid.min_elevation) / grid.elevation_spacing))
+
+
+class TestBuildPassbandGrid(unittest.TestCase):
+    """`build_passband_grid` takes a single `(energy_ratio, elevation)`-indexed
+    DataFrame and returns a `PassbandGrid` containing the resampled value
+    array, the per-elevation speed-ratio boundaries, and the elevation range."""
+
+    @classmethod
+    def setUpClass(cls):
+        # Default-Gaussian grid shared by tests that just need a generic, well-
+        # behaved passband. Built once per class because the arrays are read-only
+        # and the build (~5 ms) is the dominant cost in this test class.
+        cls.default_grid = build_passband_grid(_gaussian_values_df())
+
+    def test_output_is_resampled_to_target_axes(self):
+        """Regardless of the input DataFrame's sampling, the output is resampled
+        onto the canonical `_TARGET_ELEVATIONS` × `_TARGET_SPEED_RATIOS` grid.
+        Reconstruct the elevation and speed-ratio axes from `(min, spacing,
+        shape)` and compare against the target arrays — any mismatch in shape,
+        origin, or spacing surfaces here."""
+        grid = self.default_grid
+        n_el, n_sr = grid.values.shape
+        elevations = grid.min_elevation + grid.elevation_spacing * np.arange(n_el)
+        speed_ratios = grid.min_speed_ratio + grid.speed_ratio_spacing * np.arange(n_sr)
+        np.testing.assert_allclose(elevations, _TARGET_ELEVATIONS)
+        np.testing.assert_allclose(speed_ratios, _TARGET_SPEED_RATIOS)
+
+    def test_boundary_marks_cutoff_crossing(self):
+        """For every row inside the active elevation range, the row's value at
+        `min_boundary[i]` and `max_boundary[i]` is exactly the cutoff (linear
+        interpolation along the speed-ratio axis)."""
+        grid = self.default_grid
+        cutoff = _PASSBAND_BOUNDARY_THRESHOLD * grid.values.max()
+
+        for i in range(grid.values.shape[0]):
+            if np.isnan(grid.min_boundary[i]):
+                continue
+            with self.subTest(row=i):
+                np.testing.assert_allclose(
+                    np.interp(grid.min_boundary[i], _TARGET_SPEED_RATIOS, grid.values[i]),
+                    cutoff,
+                )
+                np.testing.assert_allclose(
+                    np.interp(grid.max_boundary[i], _TARGET_SPEED_RATIOS, grid.values[i]),
+                    cutoff,
+                )
+
+    def test_elevation_range_brackets_peak(self):
+        """Two grids built with peaks at different elevations must each have an
+        elevation range that brackets *its own* peak and excludes the other's."""
+        upper_peak = 10.0
+        lower_peak = -10.0
+        grid_high = build_passband_grid(_gaussian_values_df(peak_elevation=upper_peak))
+        grid_low = build_passband_grid(_gaussian_values_df(peak_elevation=lower_peak))
+
+        high_lo, high_hi = grid_high.elevation_range
+        low_lo, low_hi = grid_low.elevation_range
+
+        # peak in bounds
+        self.assertLess(high_lo, upper_peak)
+        self.assertLess(low_lo, lower_peak)
+        self.assertGreater(high_hi, upper_peak)
+        self.assertGreater(low_hi, lower_peak)
+
+        # opposite peak out of either the lower or higher bound
+        self.assertGreater(high_lo, lower_peak)
+        self.assertLess(low_hi, upper_peak)
+
+    def test_elevation_range_marks_cutoff_crossing(self):
+        """Rounding the default Gaussian's tails to exactly zero pushes the
+        outer rows below the cutoff. The elevation range edges must land
+        exactly where `row_max` linearly interpolates to the cutoff."""
+        df = _gaussian_values_df()
+        df["value"] = df["value"].round(2)
+        grid = build_passband_grid(df)
+
+        cutoff = _PASSBAND_BOUNDARY_THRESHOLD * grid.values.max()
+        row_max = grid.values.max(axis=1)
+
+        lo, hi = grid.elevation_range
+        np.testing.assert_allclose(np.interp(lo, _TARGET_ELEVATIONS, row_max), cutoff)
+        np.testing.assert_allclose(np.interp(hi, _TARGET_ELEVATIONS, row_max), cutoff)
+        # Range strictly tightened from the full target axis.
+        self.assertGreater(lo, _TARGET_ELEVATIONS[0])
+        self.assertLess(hi, _TARGET_ELEVATIONS[-1])
+
+    def test_grid_is_normalized_so_on_axis_value_is_one(self):
+        """`build_passband_grid` normalizes the grid so that
+        `interpolate_passband(grid, 0.0, 1.0) == 1.0`."""
+        df = _gaussian_values_df(peak_speed_ratio=1.05, sigma_er=0.05)
+        grid = build_passband_grid(df)
+        self.assertAlmostEqual(interpolate_passband(grid, 0.0, 1.0), 1.0)
+
+    def test_speed_ratio_indexing_via_k_factor(self):
+        """`_build_passband_array` converts `energy_ratio -> speed_ratio`
+        via `sqrt(energy_ratio/k*)`. Energy ratio refers to E/|V| while speed ratio
+        refers to v/v_0. v/v_0 = 1 corresponds to E/|V| = k*.
+        An input whose peak happens to be at energy_ratio = k*
+        and elevation = 0 (such as the default grid fixture)
+        must produce a grid peaking at speed_ratio = 1.0."""
+        grid = self.default_grid
+
+        row_at_peak_elevation = grid.values[
+            _row_index_for_elevation(grid, 0.0)
+        ]
+        peak_speed_ratio = _TARGET_SPEED_RATIOS[int(np.argmax(row_at_peak_elevation))]
+
+        self.assertAlmostEqual(peak_speed_ratio, 1.0, places=2)
+        self.assertGreater(row_at_peak_elevation.max(), 0.5)
+
+
+class TestInterpolatePassband(unittest.TestCase):
+    """Bilinear interpolation, zero outside the grid."""
+
+    def setUp(self):
+        self.grid = build_passband_grid(_gaussian_values_df(sigma_el=2.0))
+
+    def test_matches_corner_weighted_sum(self):
+        """`interpolate_passband` returns the bilinear-weighted sum of the four
+            enclosing corner values."""
+        cases = [
+            # (elevation, speed_ratio, label)
+            (0.0, 1.0, "on-node"),
+            (1.25, 1.005, "cell center"),
+            (1.1, 1.0014, "off-center"),
+        ]
+        for elevation, speed_ratio, label in cases:
+            with self.subTest(label=label):
+                i_float = (
+                    elevation - self.grid.min_elevation
+                ) / self.grid.elevation_spacing
+                j_float = (
+                    speed_ratio - self.grid.min_speed_ratio
+                ) / self.grid.speed_ratio_spacing
+                i_lo, j_lo = int(i_float), int(j_float)
+                i_weight, j_weight = i_float - i_lo, j_float - j_lo
+
+                corners = self.grid.values[
+                    i_lo : i_lo + 2, j_lo : j_lo + 2
+                ]
+                weights = np.array(
+                    [
+                        [(1 - i_weight) * (1 - j_weight), (1 - i_weight) * j_weight],
+                        [i_weight * (1 - j_weight), i_weight * j_weight],
+                    ]
+                )
+                expected = float((corners * weights).sum())
+
+                np.testing.assert_allclose(
+                    interpolate_passband(self.grid, elevation, speed_ratio),
+                    expected,
+                    rtol=1e-12,
+                )
+
+    def test_returns_zero_outside_grid_in_either_axis(self):
+        """Out-of-bounds query in either axis returns 0.0."""
+        for el, sr, label in [
+            (-100.0, 1.0, "below elevation"),
+            (100.0, 1.0, "above elevation"),
+            (0.0, 0.5, "below speed_ratio"),
+            (0.0, 1.5, "above speed_ratio"),
+        ]:
+            with self.subTest(label=label):
+                self.assertEqual(interpolate_passband(self.grid, el, sr), 0.0)
+
+
+class TestSpeedRatioRangeAtElevation(unittest.TestCase):
+    """`speed_ratio_range_at_elevation` returns the per-elevation min/max
+    speed-ratio bounds, reading from the grid's `min_boundary` and
+    `max_boundary` arrays."""
+
+    def setUp(self):
+        self.grid = build_passband_grid(_gaussian_values_df(peak_elevation=2.5))
+
+    def test_returns_cutoff_crossing_for_on_grid_elevation(self):
+        """At each on-grid elevation in the active range, the returned (lo, hi)
+        lands near where the passband crosses the cutoff along the speed-ratio
+        axis."""
+        grid = build_passband_grid(_gaussian_values_df())
+        cutoff = _PASSBAND_BOUNDARY_THRESHOLD * grid.values.max()
+        active_lo, active_hi = grid.elevation_range
+        for elevation in np.linspace(active_lo, active_hi, 5):
+            with self.subTest(elevation=elevation):
+                lo, hi = speed_ratio_range_at_elevation(grid, float(elevation))
+                row = grid.values[_row_index_for_elevation(grid, float(elevation))]
+                self.assertAlmostEqual(
+                    np.interp(lo, _TARGET_SPEED_RATIOS, row), cutoff, places=2
+                )
+                self.assertAlmostEqual(
+                    np.interp(hi, _TARGET_SPEED_RATIOS, row), cutoff, places=2
+                )
+
+    def test_returns_union_of_bracketing_rows_for_off_grid_elevation(self):
+        """For an elevation between two grid rows, the returned (lo, hi)
+        spans the union of the two rows' speed-ratio bounds."""
+        lower_elevation = 0.0
+        upper_elevation = 0.5
+        between_elevation = 0.25
+        lower_row = _row_index_for_elevation(self.grid, lower_elevation)
+        upper_row = _row_index_for_elevation(self.grid, upper_elevation)
+        expected_lo = min(
+            self.grid.min_boundary[lower_row], self.grid.min_boundary[upper_row]
+        )
+        expected_hi = max(
+            self.grid.max_boundary[lower_row], self.grid.max_boundary[upper_row]
+        )
+        lo, hi = speed_ratio_range_at_elevation(self.grid, between_elevation)
+        self.assertAlmostEqual(lo, expected_lo)
+        self.assertAlmostEqual(hi, expected_hi)
+
+    def test_min_is_no_greater_than_max(self):
+        """min <= max at every elevation inside the active range."""
+        active_lo, active_hi = self.grid.elevation_range
+        for elevation in np.linspace(active_lo, active_hi, 7):
+            with self.subTest(elevation=elevation):
+                lower, upper = speed_ratio_range_at_elevation(
+                    self.grid, float(elevation)
+                )
+                self.assertLessEqual(lower, upper)
+
+    def test_nan_row_handling_at_active_range_edges(self):
+        """Outside the active passband the boundary arrays carry NaN. When the
+        elevation lookup brackets a NaN row, behavior depends on how many of
+        the two bracketing rows are NaN:
+
+          - both NaN -> raises ValueError (caller failed to gate on the
+            active elevation range)
+          - exactly one NaN -> returns the valid row's bounds
+
+        Both branches must be exercised at the top edge of the active range
+        and at the bottom edge, where the asymmetric clamp gives
+        (lower_row, upper_row) = (last, last) vs (0, 1) respectively.
+        """
+        grid = build_passband_grid(_gaussian_values_df())
+
+        valid_rows = np.where(~np.isnan(grid.min_boundary))[0]
+        first_valid = int(valid_rows[0])
+        last_valid = int(valid_rows[-1])
+        n_rows = grid.min_boundary.shape[0]
+        # Sanity: the bottom-edge "both NaN" case needs rows 0 and 1 both NaN,
+        # i.e. the active range must start at row 2 or later. The top-edge
+        # case only needs the final row to be NaN.
+        self.assertGreater(first_valid, 1)
+        self.assertLess(last_valid, n_rows - 1)
+
+        spacing = grid.elevation_spacing
+        min_el = grid.min_elevation
+
+        upper_edge_one_nan = min_el + spacing * (last_valid + 0.5)
+        lower_edge_one_nan = min_el + spacing * (first_valid - 0.5)
+        past_upper_edge = min_el + spacing * n_rows + 100.0
+        past_lower_edge = min_el - 100.0
+
+        with self.subTest(case="both NaN past upper edge"):
+            with self.assertRaises(ValueError):
+                speed_ratio_range_at_elevation(grid, float(past_upper_edge))
+
+        with self.subTest(case="both NaN past lower edge"):
+            with self.assertRaises(ValueError):
+                speed_ratio_range_at_elevation(grid, float(past_lower_edge))
+
+        with self.subTest(case="only upper row NaN at top of active range"):
+            lo, hi = speed_ratio_range_at_elevation(grid, float(upper_edge_one_nan))
+            self.assertAlmostEqual(lo, float(grid.min_boundary[last_valid]))
+            self.assertAlmostEqual(hi, float(grid.max_boundary[last_valid]))
+
+        with self.subTest(case="only lower row NaN at bottom of active range"):
+            lo, hi = speed_ratio_range_at_elevation(grid, float(lower_edge_one_nan))
+            self.assertAlmostEqual(lo, float(grid.min_boundary[first_valid]))
+            self.assertAlmostEqual(hi, float(grid.max_boundary[first_valid]))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/response/test_swapi_response.py b/tests/swapi/response/test_swapi_response.py
new file mode 100644
index 000000000..777ee5ea0
--- /dev/null
+++ b/tests/swapi/response/test_swapi_response.py
@@ -0,0 +1,245 @@
+import unittest
+
+import numpy as np
+import numpy.testing as npt
+
+from imap_l3_processing.constants import (
+    ALPHA_MASS_PER_CHARGE_M_P_PER_E,
+    METERS_PER_KILOMETER,
+    PROTON_CHARGE_COULOMBS,
+    PROTON_MASS_KG,
+)
+from imap_l3_processing.swapi.constants import SWAPI_K_FACTOR
+from imap_l3_processing.swapi.response.swapi_response import (
+    ResponseGrid,
+    SwapiResponse,
+)
+from tests.test_helpers import get_test_instrument_team_data_path
+
+ESA_VOLTAGE_FOR_1KEV_PROTON = 1000.0 / SWAPI_K_FACTOR
+ESA_VOLTAGE_FOR_2KEV_PROTON = 2000.0 / SWAPI_K_FACTOR
+
+def _load_response() -> SwapiResponse:
+    return SwapiResponse.from_files(
+        get_test_instrument_team_data_path(
+            "swapi/imap_swapi_azimuthal-transmission_20260425_v001.csv"
+        ),
+        get_test_instrument_team_data_path(
+            "swapi/imap_swapi_central-effective-area_20260425_v001.csv"
+        ),
+        get_test_instrument_team_data_path(
+            "swapi/imap_swapi_passband-fit-coefficients_20260425_v001.csv"
+        ),
+    )
+
+
+class _RealResponseFixture(unittest.TestCase):
+    """Base class loading a single shared `SwapiResponse` once per class."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.response = _load_response()
+
+
+class TestWarmCacheApi(unittest.TestCase):
+    """Input-handling contract of `warm_cache`: dedup, NaN/inf skip, dimensionality,
+    determinism. Each test mutates the response's `_grid_cache`, so a fresh
+    instance is built per test."""
+
+    def setUp(self):
+        self.response = _load_response()
+
+    def test_populates_cache_with_unique_finite_voltages(self):
+        """Duplicates collapse, NaN/inf are skipped."""
+        voltages = np.array([100.0, 100.0, 200.0, 300.0, np.nan, np.inf])
+        self.response.warm_cache(voltages)
+        self.assertEqual(set(self.response._passband_grid_cache.keys()), {100.0, 200.0, 300.0})
+        self.assertEqual(len(self.response._passband_grid_cache), 3)
+
+    def test_warming_twice_at_same_voltage_is_a_noop(self):
+        """A second `warm_cache` at the same voltage keeps the existing
+        PassbandGrid objects — it does not rebuild them."""
+        self.response.warm_cache([750.0])
+        first_grids = {
+            region: self.response._passband_grid_cache[750.0][region] for region in ("SG", "OA")
+        }
+        self.response.warm_cache([750.0])
+
+        for region in ("SG", "OA"):
+            with self.subTest(region=region):
+                self.assertIs(self.response._passband_grid_cache[750.0][region], first_grids[region])
+
+    def test_accepts_2d_voltage_array(self):
+        """Multidimensional voltage inputs are flattened."""
+        voltages = np.array([[100.0, 200.0], [200.0, 300.0]])
+        self.response.warm_cache(voltages)
+        self.assertEqual(set(self.response._passband_grid_cache.keys()), {100.0, 200.0, 300.0})
+
+
+class TestPassbandInterpolation(_RealResponseFixture):
+    """Cover `_get_passband_values`: in-range it must evaluate
+    `exp(polyval(coeffs_row, log(SWAPI_K_FACTOR * V)))` with coefficients read
+    along the column axis (highest degree first); outside the per-region
+    calibration window the voltage is clamped before evaluation, which keeps
+    values physical (non-negative, near unity) even when the raw polynomial
+    extrapolation would diverge."""
+
+    # Upper bound > 1.0 because the polynomial is fit to *un-normalized*
+    # response data; normalization happens elsewhere. The fit can produce
+    # values slightly above 1.0 at evaluation points between the fit nodes.
+    PASSBAND_VALUE_UPPER_BOUND = 1.5
+
+    def test_values_match_manual_polyval(self):
+        for region in ("OA", "SG"):
+            with self.subTest(region=region):
+                v_min, v_max = self.response._passband_esa_voltage_limits[region]
+                voltage = 0.5 * (v_min + v_max)
+
+                returned = self.response._get_passband_values(voltage, region)
+
+                coeffs = self.response._passband_fit_coefficients.xs(
+                    region, level="region"
+                )
+                log_beam_energy = np.log(SWAPI_K_FACTOR * voltage)
+                n_degrees = coeffs.shape[1]
+                degrees = np.arange(n_degrees - 1, -1, -1)
+                expected_exponent = (coeffs.values * log_beam_energy ** degrees).sum(
+                    axis=1
+                )
+                expected = np.exp(expected_exponent)
+
+                npt.assert_allclose(
+                    returned["value"].to_numpy(), expected, rtol=1e-12
+                )
+
+    def _assert_grid_value_bounds(self, esa_voltage):
+        self.response.warm_cache([esa_voltage])
+        cached = self.response._passband_grid_cache[self.response._cache_key(esa_voltage)]
+        for region in ("SG", "OA"):
+            grid = cached[region]
+            self.assertGreaterEqual(
+                grid.values.min(),
+                0.0,
+                msg=f"{region} passband has negative values at {esa_voltage} V",
+            )
+            self.assertLessEqual(
+                grid.values.max(),
+                self.PASSBAND_VALUE_UPPER_BOUND,
+                msg=f"{region} passband exceeds bound at {esa_voltage} V",
+            )
+
+    def test_passband_grid_bounds_at_low_voltage(self):
+        self._assert_grid_value_bounds(50.0)
+
+    def test_passband_grid_bounds_at_high_voltage(self):
+        self._assert_grid_value_bounds(20000.0)
+
+
+class TestCreateResponseGrid(unittest.TestCase):
+    """`create_response_grid` bundles the `PassbandGrid` with additional
+    quantities (central speed and central effective area) into a `ResponseGrid`
+    for the integrator. It caches by `(voltage, species, ea_scale)`."""
+
+    def setUp(self):
+        self.response = _load_response()
+        self.voltage = ESA_VOLTAGE_FOR_1KEV_PROTON
+        self.response.warm_cache([self.voltage])
+
+    def test_raises_keyerror_when_voltage_not_warmed(self):
+        """`get_response_grid` requires a prior `warm_cache` at the same V; the
+        error message must name the voltage and the missing call so a caller
+        can recover without reading the source."""
+        fresh = _load_response()
+        unwarmed_voltage = 12345.0
+        with self.assertRaises(KeyError) as ctx:
+            fresh.get_response_grid(unwarmed_voltage, 1.0)
+        message = str(ctx.exception)
+        self.assertIn(str(unwarmed_voltage), message)
+        self.assertIn("warm_cache", message)
+
+    def test_sg_and_oa_passband_fields_share_identity_with_cached_grids(self):
+        rg = self.response.get_response_grid(self.voltage, 1.0)
+        cached = self.response._passband_grid_cache[self.response._cache_key(self.voltage)]
+        self.assertIs(rg.sg_passband, cached["SG"])
+        self.assertIs(rg.oa_passband, cached["OA"])
+
+    def test_central_speed_field_matches_proton_speed_formula(self):
+        # v = sqrt(2 · K · e · |V| / m_p), expressed in km/s.
+        expected_speed_km_s = (
+            np.sqrt(
+                2 * SWAPI_K_FACTOR * PROTON_CHARGE_COULOMBS
+                * abs(self.voltage) / PROTON_MASS_KG
+            )
+            / METERS_PER_KILOMETER
+        )
+        rg = self.response.get_response_grid(self.voltage, 1.0)
+        npt.assert_allclose(rg.central_speed, expected_speed_km_s, rtol=1e-12)
+
+    def test_central_effective_area_field_matches_csv_at_1kev_proton(self):
+        # Effective area at 1 keV proton ESA voltage from the CSV
+        # `imap_swapi_central-effective-area_20260425_v001.csv` is 0.38 cm^2.
+        rg = self.response.get_response_grid(self.voltage, 1.0)
+        npt.assert_approx_equal(rg.central_effective_area, 0.38, significant=3)
+
+    def test_azimuthal_transmission_grid_pass_through(self):
+        rg = self.response.get_response_grid(self.voltage, 1.0)
+        npt.assert_array_equal(
+            rg.azimuthal_transmission.values, self.response._azimuthal_transmission
+        )
+        self.assertEqual(
+            rg.azimuthal_transmission.spacing,
+            self.response.AZIMUTHAL_TRANSMISSION_SPACING_DEG,
+        )
+
+    def test_effective_area_scale_multiplies_central_effective_area(self):
+        """`central_effective_area_scale` is the species-specific efficiency
+        multiplier (e.g. alpha vs. proton CEM efficiency)."""
+        rg_unscaled = self.response.get_response_grid(self.voltage, 1.0)
+        rg_scaled = self.response.get_response_grid(
+            self.voltage, 1.0, central_effective_area_scale=0.42
+        )
+        npt.assert_allclose(
+            rg_scaled.central_effective_area,
+            rg_unscaled.central_effective_area * 0.42,
+        )
+
+    def test_central_speed_depends_on_species(self):
+        """Different mass-per-charge → different central speed in the same grid.
+        Reference values computed independently from CODATA constants at
+        V = 1000/K (so K·e·V = 1 keV per unit charge):
+          v_p = sqrt(2 · 1 keV · e / m_p) / 1e3 km/s
+          v_α = sqrt(2 · 1 keV · 2e / m_α) / 1e3 km/s
+        with m_p = 1.67262192595e-27 kg, m_α = 6.6446573450e-27 kg,
+        e = 1.602176634e-19 C."""
+        rg_proton = self.response.get_response_grid(self.voltage, 1.0)
+        rg_alpha = self.response.get_response_grid(
+            self.voltage, ALPHA_MASS_PER_CHARGE_M_P_PER_E
+        )
+        npt.assert_allclose(rg_proton.central_speed, 437.6947142244463, rtol=1e-12)
+        npt.assert_allclose(rg_alpha.central_speed, 310.5624166704235, rtol=1e-12)
+
+    def test_repeat_call_returns_cached_object(self):
+        """Regression: the cache key was previously a generator expression, so
+        every lookup missed and the dict grew unboundedly. Repeated calls at
+        the same args must return the *same object*, and the cache must not
+        grow."""
+        rg1 = self.response.get_response_grid(self.voltage, 1.0)
+        rg2 = self.response.get_response_grid(self.voltage, 1.0)
+        self.assertIs(rg1, rg2)
+        self.assertEqual(len(self.response._response_grid_cache), 1)
+
+    def test_cache_is_keyed_on_all_three_arguments(self):
+        """A different value in any of (voltage, species, ea_scale)
+        must produce a distinct cache entry."""
+        rg_proton = self.response.get_response_grid(self.voltage, 1.0)
+        rg_alpha = self.response.get_response_grid(self.voltage, 2.0)
+        rg_scaled = self.response.get_response_grid(
+            self.voltage, 1.0, central_effective_area_scale=0.5
+        )
+        self.assertIsNot(rg_proton, rg_alpha)
+        self.assertIsNot(rg_proton, rg_scaled)
+        self.assertEqual(len(self.response._response_grid_cache), 3)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/test_constants.py b/tests/swapi/test_constants.py
new file mode 100644
index 000000000..29f4f87d5
--- /dev/null
+++ b/tests/swapi/test_constants.py
@@ -0,0 +1,44 @@
+import unittest
+
+from imap_l3_processing.swapi.constants import (
+    SWAPI_COARSE_SWEEP_BINS,
+    SWAPI_DISCARDED_BIN,
+    SWAPI_FINE_SWEEP_BINS,
+    SWAPI_K_FACTOR,
+    SWAPI_L2_K_FACTOR,
+    SWAPI_SCIENCE_BINS,
+)
+
+
+class TestConstants(unittest.TestCase):
+    """Just because they're so subtly tricky..."""
+
+    def test_discarded_bin_is_zero(self):
+        self.assertEqual(SWAPI_DISCARDED_BIN, 0)
+
+    def test_science_bins_excludes_discarded_bin(self):
+        self.assertEqual(SWAPI_SCIENCE_BINS, slice(1, 72))
+
+    def test_coarse_and_fine_partition_science_bins(self):
+        # Coarse and fine bin slices must tile the science range with no overlap and no gap.
+        self.assertEqual(SWAPI_COARSE_SWEEP_BINS.start, SWAPI_SCIENCE_BINS.start)
+        self.assertEqual(SWAPI_COARSE_SWEEP_BINS.stop, SWAPI_FINE_SWEEP_BINS.start)
+        self.assertEqual(SWAPI_FINE_SWEEP_BINS.stop, SWAPI_SCIENCE_BINS.stop)
+
+    def test_coarse_sweep_has_62_bins_and_fine_has_9(self):
+        self.assertEqual(
+            SWAPI_COARSE_SWEEP_BINS.stop - SWAPI_COARSE_SWEEP_BINS.start, 62
+        )
+        self.assertEqual(SWAPI_FINE_SWEEP_BINS.stop - SWAPI_FINE_SWEEP_BINS.start, 9)
+
+    def test_simion_k_factor(self):
+        self.assertAlmostEqual(SWAPI_K_FACTOR, 1.89)
+
+    def test_l2_label_k_factor_differs_from_simion(self):
+        # L2 esa_energy = SWAPI_L2_K_FACTOR × |V|, divided out by L3 to recover voltage.
+        self.assertAlmostEqual(SWAPI_L2_K_FACTOR, 1.93)
+        self.assertNotAlmostEqual(SWAPI_K_FACTOR, SWAPI_L2_K_FACTOR)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/swapi/test_swapi_processor.py b/tests/swapi/test_swapi_processor.py
index 1568cbba6..91735738f 100644
--- a/tests/swapi/test_swapi_processor.py
+++ b/tests/swapi/test_swapi_processor.py
@@ -6,24 +6,20 @@
 import numpy as np
 from imap_data_access import config
 from imap_data_access.processing_input import ProcessingInputCollection, ScienceInput, AncillaryInput
-from uncertainties import ufloat
 from uncertainties.unumpy import uarray, nominal_values, std_devs
 
 from imap_l3_processing.constants import THIRTY_SECONDS_IN_NANOSECONDS, \
     FIVE_MINUTES_IN_NANOSECONDS
 from imap_l3_processing.models import InputMetadata
-from imap_l3_processing.swapi.descriptors import PROTON_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR, \
-    DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR, INSTRUMENT_RESPONSE_LOOKUP_TABLE_DESCRIPTOR, \
-    EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR, \
-    CLOCK_ANGLE_AND_FLOW_DEFLECTION_LOOKUP_TABLE_DESCRIPTOR, ALPHA_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR, \
-    GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR, GEOMETRIC_FACTOR_PUI_LOOKUP_TABLE_DESCRIPTOR
-from imap_l3_processing.swapi.l3a.models import SwapiL2Data, SwapiL3ProtonSolarWindData, SwapiL3PickupIonData, \
-    SwapiL3AlphaSolarWindData
-from imap_l3_processing.swapi.l3a.science.calculate_alpha_solar_wind_temperature_and_density import \
-    AlphaSolarWindTemperatureAndDensity
+from imap_l3_processing.swapi.descriptors import DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR, \
+    INSTRUMENT_RESPONSE_LOOKUP_TABLE_DESCRIPTOR, EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR, \
+    ALPHA_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR, \
+    GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR, GEOMETRIC_FACTOR_PUI_LOOKUP_TABLE_DESCRIPTOR, \
+    HYDROGEN_INFLOW_VECTOR_DESCRIPTOR, HELIUM_INFLOW_VECTOR_DESCRIPTOR, \
+    AZIMUTHAL_TRANSMISSION_DESCRIPTOR, CENTRAL_EFFECTIVE_AREA_DESCRIPTOR, \
+    PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR
+from imap_l3_processing.swapi.l3a.models import SwapiL2Data, SwapiL3PickupIonData
 from imap_l3_processing.swapi.l3a.science.calculate_pickup_ion import FittingParameters
-from imap_l3_processing.swapi.l3a.science.calculate_proton_solar_wind_temperature_and_density import \
-    ProtonSolarWindTemperatureAndDensity
 from imap_l3_processing.swapi.l3a.swapi_l3a_dependencies import SWAPI_L2_DESCRIPTOR, SwapiL3ADependencies
 from imap_l3_processing.swapi.l3b.science.calculate_solar_wind_vdf import DeltaMinusPlus
 from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
@@ -35,21 +31,21 @@ class TestSwapiProcessor(TestCase):
     @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3PickupIonData')
     @patch('imap_l3_processing.utils.write_cdf')
     @patch('imap_l3_processing.swapi.swapi_processor.chunk_l2_data')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_proton_solar_wind_speed')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_clock_angle')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_deflection_angle')
+    @patch('imap_l3_processing.swapi.swapi_processor.ParallelChunkRunner')
     @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3ADependencies')
     @patch('imap_l3_processing.swapi.swapi_processor.calculate_pickup_ion_values')
     @patch('imap_l3_processing.swapi.swapi_processor.calculate_ten_minute_velocities')
+    @patch('imap_l3_processing.swapi.swapi_processor.rotate_rtn_to_dps')
     @patch('imap_l3_processing.swapi.swapi_processor.calculate_helium_pui_density')
     @patch('imap_l3_processing.swapi.swapi_processor.calculate_helium_pui_temperature')
     @patch('imap_l3_processing.processor.spiceypy')
     def test_process_l3a_pui(self, mock_spicepy, mock_calculate_helium_pui_temperature,
                              mock_calculate_helium_pui_density,
+                             mock_rotate_rtn_to_dps,
                              mock_calculate_ten_minute_velocities, mock_calculate_pickup_ion,
-                             mock_swapi_l3_dependencies_class, mock_calculate_deflection_angle,
-                             mock_calculate_clock_angle,
-                             mock_calculate_proton_solar_wind_speed, mock_chunk_l2_data, mock_write_cdf,
+                             mock_swapi_l3_dependencies_class,
+                             mock_parallel_chunk_runner_class,
+                             mock_chunk_l2_data, mock_write_cdf,
                              mock_pickup_ion_data_constructor, mock_imap_attribute_manager):
         instrument = 'swapi'
         incoming_data_level = 'l2'
@@ -64,17 +60,16 @@ def test_process_l3a_pui(self, mock_spicepy, mock_calculate_helium_pui_temperatu
 
         mock_spicepy.ktotal.return_value = 0
 
-        proton_sw_speed_fit_chisq = 9
-
-        returned_proton_sw_speed = ufloat(400000, 2)
-        mock_calculate_proton_solar_wind_speed.return_value = (
-            returned_proton_sw_speed, sentinel.a, sentinel.phi, sentinel.b, proton_sw_speed_fit_chisq)
-
-        returned_proton_sw_clock_angle = ufloat(200, 0.25)
-        mock_calculate_clock_angle.return_value = returned_proton_sw_clock_angle
-
-        returned_proton_sw_deflection_angle = ufloat(5, 0.001)
-        mock_calculate_deflection_angle.return_value = returned_proton_sw_deflection_angle
+        returned_chunk_epoch = 10 + THIRTY_SECONDS_IN_NANOSECONDS
+        returned_bulk_velocity_rtn_sc = np.array([370.0, 10.0, 5.0])
+        rotated_velocity_dps = np.array([330.0, 12.0, 4.0])
+        runner_result = dict(
+            epoch=np.array([returned_chunk_epoch]),
+            proton_sw_bulk_velocity_rtn_sc=np.array([returned_bulk_velocity_rtn_sc]),
+            quality_flags=np.array([SwapiL3Flags.NONE]),
+        )
+        mock_parallel_chunk_runner_class.return_value.run.return_value = runner_result
+        mock_rotate_rtn_to_dps.return_value = rotated_velocity_dps
 
         initial_epoch = 10
 
@@ -103,9 +98,7 @@ def test_process_l3a_pui(self, mock_spicepy, mock_calculate_helium_pui_temperatu
 
         input_file_names = [
             f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{PROTON_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
             f'imap_{instrument}_{ALPHA_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{CLOCK_ANGLE_AND_FLOW_DEFLECTION_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
             f'imap_{instrument}_{GEOMETRIC_FACTOR_PUI_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
             f'imap_{instrument}_{INSTRUMENT_RESPONSE_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
             f'imap_{instrument}_{DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
@@ -132,7 +125,7 @@ def test_process_l3a_pui(self, mock_spicepy, mock_calculate_helium_pui_temperatu
         ]
 
         mock_l3a_dependencies = mock_swapi_l3_dependencies_class.fetch_dependencies.return_value
-        mock_l3a_dependencies.data = sentinel.swapi_l2_data
+        mock_l3a_dependencies.data = Mock(energy=energy)
 
         mock_manager = mock_imap_attribute_manager.return_value
 
@@ -154,8 +147,8 @@ def test_process_l3a_pui(self, mock_spicepy, mock_calculate_helium_pui_temperatu
 
         mock_chunk_l2_data.side_effect = []
 
-        mock_chunk_l2_data.assert_has_calls([call(sentinel.swapi_l2_data, 5),
-                                             call(sentinel.swapi_l2_data, 50)])
+        mock_chunk_l2_data.assert_has_calls([call(mock_l3a_dependencies.data, 5),
+                                             call(mock_l3a_dependencies.data, 50)])
 
         instrument_response_lut, geometric_factor_lut, energies, count_rates, pui_epoch, \
             sw_velocity_vector, density_of_neutral_helium_lut, efficiency_lut, hydrogen_inflow_vector, helium_inflow_vector = mock_calculate_pickup_ion.call_args.args
@@ -187,10 +180,14 @@ def test_process_l3a_pui(self, mock_spicepy, mock_calculate_helium_pui_temperatu
         self.assertEqual(expected_fitting_params, passed_in_fitting_params)
         self.assertEqual(mock_helium_inflow_vector, helium_inflow_vector)
 
-        mock_calculate_ten_minute_velocities.assert_called_with([returned_proton_sw_speed.nominal_value],
-                                                                [returned_proton_sw_deflection_angle.nominal_value],
-                                                                [returned_proton_sw_clock_angle.nominal_value],
-                                                                np.array([SwapiL3Flags.NONE]))
+        mock_rotate_rtn_to_dps.assert_called_once()
+        rotate_args = mock_rotate_rtn_to_dps.call_args.args
+        np.testing.assert_array_equal(rotate_args[0], returned_bulk_velocity_rtn_sc)
+        self.assertEqual(rotate_args[1], returned_chunk_epoch)
+
+        ten_min_args = mock_calculate_ten_minute_velocities.call_args.args
+        np.testing.assert_array_equal(ten_min_args[0], np.array([rotated_velocity_dps]))
+        self.assertEqual(ten_min_args[1], [SwapiL3Flags.NONE])
         mock_manager.add_global_attribute.assert_has_calls([call("Data_version", outgoing_version),
                                                             call("Generation_date",
                                                                  date.today().strftime("%Y%m%d")),
@@ -219,230 +216,70 @@ def test_process_l3a_pui(self, mock_spicepy, mock_calculate_helium_pui_temperatu
         mock_write_cdf.assert_called_once_with(str(expected_cdf_path), pickup_ion_data, mock_manager)
         self.assertEqual([expected_cdf_path], product)
 
-    @patch('imap_l3_processing.utils.ImapAttributeManager')
-    @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3PickupIonData')
-    @patch('imap_l3_processing.utils.write_cdf')
-    @patch('imap_l3_processing.swapi.swapi_processor.chunk_l2_data')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_proton_solar_wind_speed')
-    @patch('imap_l3_processing.swapi.swapi_processor.estimate_deflection_and_clock_angles')
-    @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3ADependencies')
+    @patch('imap_l3_processing.swapi.swapi_processor.calculate_helium_pui_temperature')
+    @patch('imap_l3_processing.swapi.swapi_processor.calculate_helium_pui_density')
     @patch('imap_l3_processing.swapi.swapi_processor.calculate_pickup_ion_values')
     @patch('imap_l3_processing.swapi.swapi_processor.calculate_ten_minute_velocities')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_helium_pui_density')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_helium_pui_temperature')
-    @patch('imap_l3_processing.processor.spiceypy')
-    def test_process_l3a_pui_proton_sw_fit_chisq_too_large_with_pui_quality_flags(self, mock_spicepy,
-                                                                                  mock_calculate_helium_pui_temperature,
-                                                                                  mock_calculate_helium_pui_density,
-                                                                                  mock_calculate_ten_minute_velocities,
-                                                                                  mock_calculate_pickup_ion,
-                                                                                  mock_swapi_l3_dependencies_class,
-                                                                                  mock_estimate_deflection_and_clock_angles,
-                                                                                  mock_calculate_proton_solar_wind_speed,
-                                                                                  mock_chunk_l2_data,
-                                                                                  mock_write_cdf,
-                                                                                  mock_pickup_ion_data_constructor,
-                                                                                  mock_imap_attribute_manager):
-        instrument = 'swapi'
-        incoming_data_level = 'l2'
-        dependency_start_date = datetime.strftime(datetime(2025, 1, 1), "%Y%m%d")
-        version = 'v001'
-        end_date = datetime(2025, 9, 26)
-        outgoing_data_level = "l3a"
-        start_date = datetime(2025, 9, 25)
-        input_version = "v123"
-        outgoing_version = "123"
-        start_date_as_str = datetime.strftime(start_date, "%Y%m%d")
-
-        mock_spicepy.ktotal.return_value = 0
-
-        proton_sw_speed_fit_chisq = 11
-
-        returned_proton_sw_speed = ufloat(400000, 2)
-        mock_calculate_proton_solar_wind_speed.return_value = (
-            returned_proton_sw_speed, sentinel.a, sentinel.phi, sentinel.b, proton_sw_speed_fit_chisq)
-
-        returned_proton_sw_clock_angle = 200
-        returned_proton_sw_deflection_angle = 5
-
-        mock_estimate_deflection_and_clock_angles.return_value = \
-            (returned_proton_sw_deflection_angle, returned_proton_sw_clock_angle)
-
+    @patch('imap_l3_processing.swapi.swapi_processor.ParallelChunkRunner')
+    def test_process_l3a_pui_combines_proton_and_pui_quality_flags(self,
+                                                                   mock_parallel_chunk_runner_class,
+                                                                   mock_calculate_ten_minute_velocities,
+                                                                   mock_calculate_pickup_ion,
+                                                                   mock_calculate_helium_pui_density,
+                                                                   mock_calculate_helium_pui_temperature):
         initial_epoch = 10
-
-        epoch = np.array([initial_epoch, 11, 12, 13])
-        epoch_for_fifty_sweeps = np.arange(initial_epoch, 50)
-        energy = np.array([15000, 16000, 17000, 18000, 19000])
-        coincidence_count_rate = np.array(
-            [[4, 5, 6, 7, 8], [9, 10, 11, 12, 13], [14, 15, 16, 17, 18], [19, 20, 21, 22, 23]])
-        coincidence_count_rate_uncertainty = np.array(
-            [[0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5],
-             [0.1, 0.2, 0.3, 0.4, 0.5]])
-
-        chunk_of_five = SwapiL2Data(epoch, energy, coincidence_count_rate,
-                                    coincidence_count_rate_uncertainty)
-        chunk_of_fifty = SwapiL2Data(epoch_for_fifty_sweeps, energy * 2, coincidence_count_rate * 2,
-                                     coincidence_count_rate_uncertainty * 2)
-
-        expected_fitting_params = FittingParameters(1, 2, 3, 4, SwapiL3Flags.HI_CHI_SQ)
-        mock_calculate_pickup_ion.return_value = expected_fitting_params
+        epoch = np.arange(initial_epoch, initial_epoch + 50)
+        energy = np.tile(np.array([15000, 16000, 17000, 18000, 19000]), (50, 1))
+        coincidence_count_rate = np.full((50, 5), 5.0)
+        coincidence_count_rate_uncertainty = np.full((50, 5), 0.1)
+        chunk_of_fifty = SwapiL2Data(epoch, energy, coincidence_count_rate,
+                                     coincidence_count_rate_uncertainty)
+
+        runner_quality_flag = SwapiL3Flags.FIT_ERROR
+        mock_parallel_chunk_runner_class.return_value.run.return_value = dict(
+            epoch=np.array([initial_epoch + THIRTY_SECONDS_IN_NANOSECONDS]),
+            proton_sw_bulk_velocity_rtn_sc=np.array([[400.0, 10.0, 5.0]]),
+            quality_flags=np.array([runner_quality_flag]),
+        )
+
+        ten_min_quality_flag = SwapiL3Flags.BAD_FIT
+        mock_calculate_ten_minute_velocities.return_value = (
+            np.array([[17, 18, 19]]), np.array([ten_min_quality_flag]))
+
+        pui_fit_quality_flag = SwapiL3Flags.PUI_FIT_MISSING_UNCERTAINTY
+        mock_calculate_pickup_ion.return_value = FittingParameters(1, 2, 3, 4, pui_fit_quality_flag)
         mock_calculate_helium_pui_density.return_value = 5
         mock_calculate_helium_pui_temperature.return_value = 6
 
-        pui_quality_flags = [SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED]
-        mock_calculate_ten_minute_velocities.return_value = (np.array([[17, 18, 19]]),
-                                                             pui_quality_flags)
-
-        science_input = ScienceInput(
-            f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf')
-
-        input_file_names = [
-            f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{PROTON_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{ALPHA_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{CLOCK_ANGLE_AND_FLOW_DEFLECTION_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{GEOMETRIC_FACTOR_PUI_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{INSTRUMENT_RESPONSE_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-        ]
+        with patch('imap_l3_processing.swapi.swapi_processor.rotate_rtn_to_dps', return_value=np.array([330.0, 12.0, 4.0])):
+            input_metadata = InputMetadata('swapi', 'l3a', datetime(2025, 6, 12), datetime(2025, 6, 13), 'v123')
+            swapi_processor = SwapiProcessor(Mock(), input_metadata)
+            product = swapi_processor.process_l3a_pui(data=chunk_of_fifty, dependencies=Mock())
 
-        ancillary_inputs = [AncillaryInput(file_name) for file_name in input_file_names[1:]]
-
-        dependencies = ProcessingInputCollection(science_input, *ancillary_inputs)
-
-        input_metadata = InputMetadata(instrument, outgoing_data_level, start_date, end_date, input_version)
-
-        pickup_ion_data = mock_pickup_ion_data_constructor.return_value
-        expected_pickup_ion_metadata = replace(input_metadata, descriptor="pui-he")
-        pickup_ion_data.input_metadata = expected_pickup_ion_metadata
-
-        input_metadata.descriptor = "pui-he"
+        # The runner's per-window proton-fit quality flag is passed through to calculate_ten_minute_velocities.
+        self.assertEqual([runner_quality_flag],
+                         mock_calculate_ten_minute_velocities.call_args.args[1])
 
-        expected_cdf_path = (config["DATA_DIR"] / "imap" / "swapi" / "l3a" / "2025" / "09" /
-                             f"imap_swapi_l3a_pui-he_{start_date_as_str}_{input_version}.cdf")
-
-        mock_chunk_l2_data.side_effect = [
-            [chunk_of_five],
-            [chunk_of_fifty],
-        ]
-
-        mock_l3a_dependencies = mock_swapi_l3_dependencies_class.fetch_dependencies.return_value
-        mock_l3a_dependencies.data = sentinel.swapi_l2_data
-
-        mock_manager = mock_imap_attribute_manager.return_value
-
-        swapi_processor = SwapiProcessor(
-            dependencies, input_metadata)
-        product = swapi_processor.process()
-
-        actual_science_input = swapi_processor.dependencies.get_science_inputs()[0]
-        self.assertEqual(actual_science_input.get_time_range()[0].strftime("%Y%m%d"), dependency_start_date)
-
-        mock_swapi_l3_dependencies_class.fetch_dependencies.assert_called_once_with(dependencies)
-
-        mock_instrument_response_calibration_table = mock_l3a_dependencies.instrument_response_calibration_table
-        mock_geometric_factor_calibration_table = mock_l3a_dependencies.geometric_factor_calibration_table
-        mock_efficiency_lut = mock_l3a_dependencies.efficiency_calibration_table
-        mock_density_of_neutral_helium_calibration_table = mock_l3a_dependencies.density_of_neutral_helium_calibration_table
-        mock_hydrogen_inflow_vector = mock_l3a_dependencies.hydrogen_inflow_vector
-        mock_helium_inflow_vector = mock_l3a_dependencies.helium_inflow_vector
-
-        mock_chunk_l2_data.assert_has_calls([call(sentinel.swapi_l2_data, 5),
-                                             call(sentinel.swapi_l2_data, 50)])
-
-        coincident_rates, l2_energy, sci_start_time = mock_calculate_proton_solar_wind_speed.call_args.args
-
-        np.testing.assert_array_equal(chunk_of_five.coincidence_count_rate, nominal_values(coincident_rates))
-        np.testing.assert_array_equal(chunk_of_five.coincidence_count_rate_uncertainty,
-                                      std_devs(coincident_rates))
-        np.testing.assert_array_equal(chunk_of_five.energy, l2_energy)
-        np.testing.assert_array_equal(chunk_of_five.sci_start_time, sci_start_time)
-
-        mock_estimate_deflection_and_clock_angles.assert_called_once_with(returned_proton_sw_speed.nominal_value)
-
-        mock_calculate_ten_minute_velocities.assert_called_with([returned_proton_sw_speed.nominal_value],
-                                                                [returned_proton_sw_deflection_angle],
-                                                                [returned_proton_sw_clock_angle],
-                                                                np.array([SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED]))
-
-        instrument_response_lut, geometric_factor_lut, energies, count_rates, pui_epoch, \
-            sw_velocity_vector, density_of_neutral_helium_lut, efficiency_lut, hydrogen_inflow_vector, helium_inflow_vector = mock_calculate_pickup_ion.call_args.args
-
-        self.assertEqual(mock_instrument_response_calibration_table, instrument_response_lut)
-        self.assertEqual(mock_efficiency_lut, efficiency_lut)
-        self.assertEqual(mock_geometric_factor_calibration_table, geometric_factor_lut)
-        self.assertEqual(mock_density_of_neutral_helium_calibration_table, density_of_neutral_helium_lut)
-        np.testing.assert_array_equal(chunk_of_fifty.energy, energies)
-        np.testing.assert_array_equal(chunk_of_fifty.coincidence_count_rate, count_rates)
-        self.assertEqual(chunk_of_fifty.sci_start_time[0] + FIVE_MINUTES_IN_NANOSECONDS, pui_epoch)
-        self.assertEqual(mock_hydrogen_inflow_vector, hydrogen_inflow_vector)
-        self.assertEqual(mock_helium_inflow_vector, helium_inflow_vector)
-        np.testing.assert_array_equal([17, 18, 19], sw_velocity_vector)
-
-        actual_he_epoch, sw_velocity_vector, density_of_neutral_helium_lut, passed_in_fitting_params, helium_inflow_vector = mock_calculate_helium_pui_density.call_args.args
-
-        self.assertEqual(chunk_of_fifty.sci_start_time[0] + FIVE_MINUTES_IN_NANOSECONDS, actual_he_epoch)
-        np.testing.assert_array_equal([17, 18, 19], sw_velocity_vector)
-        self.assertEqual(mock_density_of_neutral_helium_calibration_table, density_of_neutral_helium_lut)
-        self.assertEqual(expected_fitting_params, passed_in_fitting_params)
-        self.assertEqual(mock_helium_inflow_vector, helium_inflow_vector)
-
-        actual_he_epoch, sw_velocity_vector, density_of_neutral_helium_lut, passed_in_fitting_params, helium_inflow_vector = mock_calculate_helium_pui_temperature.call_args.args
-
-        self.assertEqual(chunk_of_fifty.sci_start_time[0] + FIVE_MINUTES_IN_NANOSECONDS, actual_he_epoch)
-        np.testing.assert_array_equal([17, 18, 19], sw_velocity_vector)
-        self.assertEqual(mock_density_of_neutral_helium_calibration_table, density_of_neutral_helium_lut)
-        self.assertEqual(expected_fitting_params, passed_in_fitting_params)
-        self.assertEqual(mock_helium_inflow_vector, helium_inflow_vector)
-
-        mock_manager.add_global_attribute.assert_has_calls([call("Data_version", outgoing_version),
-                                                            call("Generation_date",
-                                                                 date.today().strftime("%Y%m%d")),
-                                                            call("Logical_source",
-                                                                 f"imap_swapi_l3a_pui-he"),
-                                                            call("Logical_file_id",
-                                                                 f"imap_swapi_l3a_pui-he_{start_date_as_str}_{input_version}"),
-                                                            ])
-
-        actual_pui_metadata, actual_pui_epoch, actual_pui_cooling_index, actual_pui_ionization_rate, \
-            actual_pui_cutoff_speed, actual_pui_background_rate, actual_pui_density, actual_pui_temperature, \
-            actual_quality_flags = mock_pickup_ion_data_constructor.call_args.args
-        self.assertEqual(expected_pickup_ion_metadata, actual_pui_metadata)
-        np.testing.assert_array_equal(np.array([initial_epoch + FIVE_MINUTES_IN_NANOSECONDS]), actual_pui_epoch)
-        np.testing.assert_array_equal(np.array([1]), actual_pui_cooling_index)
-        np.testing.assert_array_equal(np.array([2]), actual_pui_ionization_rate)
-        np.testing.assert_array_equal(np.array([3]), actual_pui_cutoff_speed)
-        np.testing.assert_array_equal(np.array([4]), actual_pui_background_rate)
-        np.testing.assert_array_equal(np.array([5]), actual_pui_density)
-        np.testing.assert_array_equal(np.array([6]), actual_pui_temperature)
-
-        expected_swapi_flag = SwapiL3Flags.HI_CHI_SQ | SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED
-        np.testing.assert_array_equal(actual_quality_flags, np.array(expected_swapi_flag))
-
-        mock_manager.add_instrument_attrs.assert_called_once_with("swapi", "l3a", "pui-he")
-
-        self.assertEqual(input_file_names, pickup_ion_data.parent_file_names)
-        mock_write_cdf.assert_called_once_with(str(expected_cdf_path), pickup_ion_data, mock_manager)
-        self.assertEqual([expected_cdf_path], product)
+        # The output flag is the OR of the per-window proton flag (returned by ten-min) and the per-window PUI fit flag.
+        np.testing.assert_array_equal(
+            product.quality_flags,
+            np.array([ten_min_quality_flag | pui_fit_quality_flag]),
+        )
 
     @patch('imap_l3_processing.utils.ImapAttributeManager')
     @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3ProtonSolarWindData')
     @patch('imap_l3_processing.utils.write_cdf')
+    @patch('imap_l3_processing.swapi.swapi_processor.ParallelChunkRunner')
     @patch('imap_l3_processing.swapi.swapi_processor.chunk_l2_data')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_proton_solar_wind_speed')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_proton_solar_wind_temperature_and_density')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_clock_angle')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_deflection_angle')
     @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3ADependencies')
     @patch('imap_l3_processing.processor.spiceypy')
     def test_process_l3a_proton(self, mock_spicepy,
-                                mock_swapi_l3_dependencies_class, mock_calculate_deflection_angle,
-                                mock_calculate_clock_angle,
-                                mock_proton_calculate_temperature_and_density,
-                                mock_calculate_proton_solar_wind_speed, mock_chunk_l2_data, mock_write_cdf,
+                                mock_swapi_l3_dependencies_class,
+                                mock_chunk_l2_data,
+                                mock_parallel_chunk_runner_class,
+                                mock_write_cdf,
                                 mock_proton_solar_wind_data_constructor,
-                                mock_imap_attribute_manager
-                                ):
+                                mock_imap_attribute_manager):
         instrument = 'swapi'
         incoming_data_level = 'l2'
         dependency_start_date = datetime.strftime(datetime(2025, 1, 1), "%Y%m%d")
@@ -456,23 +293,6 @@ def test_process_l3a_proton(self, mock_spicepy,
 
         mock_spicepy.ktotal.return_value = 0
 
-        returned_proton_sw_speed = ufloat(400000, 2)
-        returned_chi_sq = 5
-        mock_calculate_proton_solar_wind_speed.return_value = (
-            returned_proton_sw_speed, sentinel.a, sentinel.phi, sentinel.b, returned_chi_sq)
-
-        returned_proton_sw_temp = ufloat(99000, 1000)
-        returned_proton_sw_density = ufloat(4.97, 0.25)
-
-        mock_proton_calculate_temperature_and_density.return_value = ProtonSolarWindTemperatureAndDensity(
-            temperature=returned_proton_sw_temp, density=returned_proton_sw_density, bad_fit_flag=SwapiL3Flags.NONE)
-
-        returned_proton_sw_clock_angle = ufloat(200, 0.25)
-        mock_calculate_clock_angle.return_value = returned_proton_sw_clock_angle
-
-        returned_proton_sw_deflection_angle = ufloat(5, 0.001)
-        mock_calculate_deflection_angle.return_value = returned_proton_sw_deflection_angle
-
         initial_epoch = 10
 
         epoch = np.array([initial_epoch, 11, 12, 13])
@@ -489,14 +309,40 @@ def test_process_l3a_proton(self, mock_spicepy,
         science_input = ScienceInput(
             f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf')
 
+        runner_result = dict(
+            epoch=np.array([initial_epoch + THIRTY_SECONDS_IN_NANOSECONDS]),
+            proton_sw_speed=np.array([400000.0]),
+            proton_sw_speed_uncert=np.array([2.0]),
+            proton_sw_speed_sun=np.array([401000.0]),
+            proton_sw_speed_sun_uncert=np.array([2.5]),
+            proton_sw_temperature=np.array([99000.0]),
+            proton_sw_temperature_uncert=np.array([1000.0]),
+            proton_sw_density=np.array([4.97]),
+            proton_sw_density_uncert=np.array([0.25]),
+            proton_sw_clock_angle=np.array([200.0]),
+            proton_sw_clock_angle_uncert=np.array([0.25]),
+            proton_sw_deflection_angle=np.array([5.0]),
+            proton_sw_deflection_angle_uncert=np.array([0.001]),
+            proton_sw_bulk_velocity_rtn_sun=np.array([[400.0, 10.0, 5.0]]),
+            proton_sw_bulk_velocity_rtn_sun_covariance=np.array([np.eye(3)]),
+            proton_sw_bulk_velocity_rtn_sc=np.array([[370.0, 10.0, 5.0]]),
+            proton_sw_bulk_velocity_rtn_sc_covariance=np.array([np.eye(3)]),
+            quality_flags=np.array([SwapiL3Flags.NONE]),
+        )
+        mock_runner = mock_parallel_chunk_runner_class.return_value
+        mock_runner.run.return_value = runner_result
+
         input_file_names = [
             f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{PROTON_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{ALPHA_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{CLOCK_ANGLE_AND_FLOW_DEFLECTION_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f'imap_{instrument}_{EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f'imap_{instrument}_{GEOMETRIC_FACTOR_PUI_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
             f'imap_{instrument}_{INSTRUMENT_RESPONSE_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
             f'imap_{instrument}_{DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f'imap_{instrument}_{HYDROGEN_INFLOW_VECTOR_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f'imap_{instrument}_{HELIUM_INFLOW_VECTOR_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f'imap_{instrument}_{AZIMUTHAL_TRANSMISSION_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f'imap_{instrument}_{CENTRAL_EFFECTIVE_AREA_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f'imap_{instrument}_{PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
         ]
 
         ancillary_inputs = [AncillaryInput(file_name) for file_name in input_file_names[1:]]
@@ -514,96 +360,34 @@ def test_process_l3a_proton(self, mock_spicepy,
         expected_cdf_path = (config["DATA_DIR"] / "imap" / "swapi" / "l3a" / "2025" / "06" /
                              f"imap_swapi_l3a_proton-sw_{start_date_as_str}_{input_version}.cdf")
 
-        mock_chunk_l2_data.side_effect = [
-            [chunk_of_five],
-        ]
+        mock_chunk_l2_data.return_value = [chunk_of_five]
 
         swapi_l3a_dependencies = create_swapi_l3a_dependencies_with_mocks()
+        swapi_l3a_dependencies.data = Mock(energy=energy)
         mock_swapi_l3_dependencies_class.fetch_dependencies.return_value = swapi_l3a_dependencies
 
         mock_manager = mock_imap_attribute_manager.return_value
 
-        swapi_processor = SwapiProcessor(
-            dependencies, input_metadata)
+        swapi_processor = SwapiProcessor(dependencies, input_metadata)
         product = swapi_processor.process()
 
-        actual_science_input = swapi_processor.dependencies.get_science_inputs()[0]
-        self.assertEqual(actual_science_input.get_time_range()[0].strftime("%Y%m%d"), dependency_start_date)
-
         mock_swapi_l3_dependencies_class.fetch_dependencies.assert_called_once_with(dependencies)
 
-        mock_proton_temperature_density_calibration_table = mock_swapi_l3_dependencies_class.fetch_dependencies.return_value.proton_temperature_density_calibration_table
-        mock_clock_angle_and_flow_deflection_calibration_table = mock_swapi_l3_dependencies_class.fetch_dependencies.return_value.clock_angle_and_flow_deflection_calibration_table
-
-        mock_chunk_l2_data.assert_has_calls([call(swapi_l3a_dependencies.data, 5)])
-
-        expected_count_rate_with_uncertainties = uarray(coincidence_count_rate,
-                                                        coincidence_count_rate_uncertainty)
-        np.testing.assert_array_equal(nominal_values(expected_count_rate_with_uncertainties),
-                                      nominal_values(
-                                          mock_calculate_proton_solar_wind_speed.call_args_list[0].args[0]))
-        np.testing.assert_array_equal(std_devs(expected_count_rate_with_uncertainties),
-                                      std_devs(
-                                          mock_calculate_proton_solar_wind_speed.call_args_list[0].args[0]))
-        np.testing.assert_array_equal(energy, mock_calculate_proton_solar_wind_speed.call_args_list[0].args[1])
-        np.testing.assert_array_equal(epoch, mock_calculate_proton_solar_wind_speed.call_args_list[0].args[2])
-
-        self.assertEqual(
-            call(mock_clock_angle_and_flow_deflection_calibration_table, returned_proton_sw_speed, sentinel.a,
-                 sentinel.phi, sentinel.b), mock_calculate_clock_angle.call_args)
-
-        self.assertEqual(
-            call(mock_clock_angle_and_flow_deflection_calibration_table, returned_proton_sw_speed, sentinel.a,
-                 sentinel.phi, sentinel.b), mock_calculate_deflection_angle.call_args)
-
-        self.assertEqual(mock_proton_temperature_density_calibration_table,
-                         mock_proton_calculate_temperature_and_density.call_args_list[0].args[0])
-        self.assert_ufloat_equal(returned_proton_sw_speed,
-                                 mock_proton_calculate_temperature_and_density.call_args_list[0].args[1])
-        self.assert_ufloat_equal(returned_proton_sw_deflection_angle,
-                                 mock_proton_calculate_temperature_and_density.call_args_list[0].args[2])
-        self.assertEqual(returned_proton_sw_clock_angle,
-                         mock_proton_calculate_temperature_and_density.call_args_list[0].args[3])
-        np.testing.assert_array_equal(nominal_values(expected_count_rate_with_uncertainties),
-                                      nominal_values(
-                                          mock_proton_calculate_temperature_and_density.call_args_list[0].args[
-                                              4]))
-        np.testing.assert_array_equal(std_devs(expected_count_rate_with_uncertainties),
-                                      std_devs(
-                                          mock_proton_calculate_temperature_and_density.call_args_list[0].args[
-                                              4]))
-        np.testing.assert_array_equal(energy,
-                                      mock_proton_calculate_temperature_and_density.call_args_list[0].args[5])
-        self.assertEqual(swapi_l3a_dependencies.efficiency_calibration_table.get_proton_efficiency_for.return_value,
-                         mock_proton_calculate_temperature_and_density.call_args_list[0].args[6])
-
-        swapi_l3a_dependencies.efficiency_calibration_table.get_proton_efficiency_for.assert_called_once_with(
-            initial_epoch + THIRTY_SECONDS_IN_NANOSECONDS)
-
-        (actual_proton_metadata, actual_proton_epoch, actual_proton_sw_speed, actual_proton_sw_temperature,
-         actual_proton_sw_density, actual_proton_sw_clock_angle,
-         actual_proton_sw_deflection_angle,
-         actual_quality_flags) = mock_proton_solar_wind_data_constructor.call_args.args
-
-        self.assertEqual(expected_proton_metadata, actual_proton_metadata)
-
-        np.testing.assert_array_equal(np.array([initial_epoch + THIRTY_SECONDS_IN_NANOSECONDS]),
-                                      actual_proton_epoch,
-                                      strict=True)
-        np.testing.assert_array_equal(np.array([returned_proton_sw_speed]), actual_proton_sw_speed, strict=True)
+        swapi_l3a_dependencies.swapi_response.warm_cache.assert_called_once()
 
-        self.assert_ufloat_array([returned_proton_sw_temp], actual_proton_sw_temperature, )
+        mock_parallel_chunk_runner_class.assert_called_once_with(
+            swapi_l3a_dependencies.swapi_response,
+            swapi_l3a_dependencies.efficiency_calibration_table,
+        )
+        mock_runner.run.assert_called_once()
+        actual_chunks = mock_runner.run.call_args.args[0]
+        self.assertEqual([chunk_of_five], actual_chunks)
 
-        self.assert_ufloat_array([returned_proton_sw_density], actual_proton_sw_density)
-
-        np.testing.assert_array_equal(np.array([returned_proton_sw_clock_angle]), actual_proton_sw_clock_angle,
-                                      strict=True)
-        np.testing.assert_array_equal(np.array([returned_proton_sw_deflection_angle]),
-                                      actual_proton_sw_deflection_angle,
-                                      strict=True)
-        np.testing.assert_array_equal(np.array([SwapiL3Flags.NONE]),
-                                      actual_quality_flags,
-                                      strict=True)
+        actual_kwargs = mock_proton_solar_wind_data_constructor.call_args.kwargs
+        actual_positional = mock_proton_solar_wind_data_constructor.call_args.args
+        self.assertEqual(expected_proton_metadata, actual_positional[0])
+        for key, expected_val in runner_result.items():
+            np.testing.assert_array_equal(expected_val, actual_kwargs[key])
 
         mock_manager.add_global_attribute.assert_has_calls([call("Data_version", outgoing_version),
                                                             call("Generation_date",
@@ -620,219 +404,13 @@ def test_process_l3a_proton(self, mock_spicepy,
         mock_write_cdf.assert_called_once_with(str(expected_cdf_path), proton_solar_wind_data, mock_manager)
         self.assertEqual([expected_cdf_path], product)
 
-    @patch('imap_l3_processing.utils.ImapAttributeManager')
-    @patch('imap_l3_processing.utils.write_cdf')
-    @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3ProtonSolarWindData')
-    @patch('imap_l3_processing.swapi.swapi_processor.chunk_l2_data')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_proton_solar_wind_speed')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_proton_solar_wind_temperature_and_density')
-    @patch('imap_l3_processing.swapi.swapi_processor.estimate_deflection_and_clock_angles')
-    @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3ADependencies')
-    @patch('imap_l3_processing.processor.spiceypy')
-    def test_process_l3a_proton_estimates_angles_if_chisq_too_high_with_hi_chi_sq_flag(self, mock_spicepy,
-                                                                                       mock_swapi_l3_dependencies_class,
-                                                                                       mock_estimate_deflection_and_clock_angles,
-                                                                                       mock_proton_calculate_temperature_and_density,
-                                                                                       mock_calculate_proton_solar_wind_speed,
-                                                                                       mock_chunk_l2_data,
-                                                                                       mock_proton_solar_wind_data_constructor,
-                                                                                       _,
-                                                                                       __
-                                                                                       ):
-        instrument = 'swapi'
-        incoming_data_level = 'l2'
-        dependency_start_date = datetime.strftime(datetime(2025, 1, 1), "%Y%m%d")
-        version = 'v001'
-        end_date = datetime(2025, 6, 13)
-        outgoing_data_level = "l3a"
-        start_date = datetime(2025, 6, 12)
-        input_version = "v123"
-
-        mock_spicepy.ktotal.return_value = 0
-
-        returned_proton_sw_speed = ufloat(400000, 2)
-        returned_chi_sq = 15
-        mock_calculate_proton_solar_wind_speed.return_value = (
-            returned_proton_sw_speed, sentinel.a, sentinel.phi, sentinel.b, returned_chi_sq)
-
-        mock_estimate_deflection_and_clock_angles.return_value = (2, 270)
-
-        returned_proton_sw_temp = ufloat(99000, 1000)
-        returned_proton_sw_density = ufloat(4.97, 0.25)
-        mock_proton_calculate_temperature_and_density.return_value = ProtonSolarWindTemperatureAndDensity(
-            returned_proton_sw_temp, returned_proton_sw_density, SwapiL3Flags.HI_CHI_SQ)
-
-        initial_epoch = 10
-
-        epoch = np.array([initial_epoch, 11, 12, 13])
-        energy = np.array([15000, 16000, 17000, 18000, 19000])
-        coincidence_count_rate = np.array(
-            [[4, 5, 6, 7, 8], [9, 10, 11, 12, 13], [14, 15, 16, 17, 18], [19, 20, 21, 22, 23]])
-        coincidence_count_rate_uncertainty = np.array(
-            [[0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5],
-             [0.1, 0.2, 0.3, 0.4, 0.5]])
-
-        chunk_of_five = SwapiL2Data(epoch, energy, coincidence_count_rate,
-                                    coincidence_count_rate_uncertainty)
-
-        science_input = ScienceInput(
-            f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf')
-
-        input_file_names = [
-            f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{PROTON_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{ALPHA_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{CLOCK_ANGLE_AND_FLOW_DEFLECTION_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{INSTRUMENT_RESPONSE_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-        ]
-
-        ancillary_inputs = [AncillaryInput(file_name) for file_name in input_file_names[1:]]
-
-        dependencies = ProcessingInputCollection(science_input, *ancillary_inputs)
-
-        input_metadata = InputMetadata(instrument, outgoing_data_level, start_date, end_date, input_version)
-
-        proton_solar_wind_data = mock_proton_solar_wind_data_constructor.return_value
-        expected_proton_metadata = replace(input_metadata, descriptor="proton-sw")
-        proton_solar_wind_data.input_metadata = expected_proton_metadata
-
-        input_metadata.descriptor = "proton-sw"
-
-        mock_chunk_l2_data.side_effect = [
-            [chunk_of_five],
-        ]
-
-        swapi_l3a_dependencies = create_swapi_l3a_dependencies_with_mocks()
-        mock_swapi_l3_dependencies_class.fetch_dependencies.return_value = swapi_l3a_dependencies
-
-        swapi_processor = SwapiProcessor(
-            dependencies, input_metadata)
-        product = swapi_processor.process()
-
-        mock_proton_temperature_density_calibration_table = mock_swapi_l3_dependencies_class.fetch_dependencies.return_value.proton_temperature_density_calibration_table
-        mock_clock_angle_and_flow_deflection_calibration_table = mock_swapi_l3_dependencies_class.fetch_dependencies.return_value.clock_angle_and_flow_deflection_calibration_table
-
-        self.assertEqual(mock_proton_temperature_density_calibration_table,
-                         mock_proton_calculate_temperature_and_density.call_args_list[0].args[0])
-        self.assert_ufloat_equal(returned_proton_sw_speed,
-                                 mock_proton_calculate_temperature_and_density.call_args_list[0].args[1])
-        self.assertEqual(2,
-                         mock_proton_calculate_temperature_and_density.call_args_list[0].args[2].nominal_value)
-        np.testing.assert_equal(45,
-                                mock_proton_calculate_temperature_and_density.call_args_list[0].args[2].std_dev)
-        self.assertEqual(270,
-                         mock_proton_calculate_temperature_and_density.call_args_list[0].args[3].nominal_value)
-        np.testing.assert_equal(180,
-                                mock_proton_calculate_temperature_and_density.call_args_list[0].args[3].std_dev)
-        mock_estimate_deflection_and_clock_angles.assert_called_with(returned_proton_sw_speed.nominal_value)
-
-        (actual_proton_metadata, actual_proton_epoch, actual_proton_sw_speed, actual_proton_sw_temperature,
-         actual_proton_sw_density, actual_proton_sw_clock_angle,
-         actual_proton_sw_deflection_angle,
-         actual_quality_flags) = mock_proton_solar_wind_data_constructor.call_args.args
-
-        self.assertEqual(expected_proton_metadata, actual_proton_metadata)
-
-        np.testing.assert_array_equal(np.array([initial_epoch + THIRTY_SECONDS_IN_NANOSECONDS]),
-                                      actual_proton_epoch,
-                                      strict=True)
-        self.assert_ufloat_array(np.array([returned_proton_sw_speed]), actual_proton_sw_speed)
-        self.assert_ufloat_array(np.array([returned_proton_sw_temp]), actual_proton_sw_temperature)
-        self.assert_ufloat_array(np.array([returned_proton_sw_density]), actual_proton_sw_density)
-
-        np.testing.assert_array_equal(nominal_values(actual_proton_sw_clock_angle), [270])
-        np.testing.assert_array_equal(std_devs(actual_proton_sw_clock_angle), [180])
-
-        np.testing.assert_array_equal(nominal_values(actual_proton_sw_deflection_angle), [2])
-        np.testing.assert_array_equal(std_devs(actual_proton_sw_deflection_angle), [45])
-
-        np.testing.assert_array_equal(np.array([SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED | SwapiL3Flags.HI_CHI_SQ]),
-                                      actual_quality_flags,
-                                      strict=True)
-
-    def test_process_l3a_proton_outputs_fill_for_chunks_with_fill(self):
-        instrument = 'swapi'
-        end_date = datetime(2025, 6, 13)
-        outgoing_data_level = "l3a"
-        start_date = datetime(2025, 6, 12)
-        input_version = "v123"
-        initial_epoch = 10
-
-        epoch = np.array([initial_epoch, 11, 12, 13])
-        energy = np.array([15000, 16000, 17000, 18000, 19000])
-        coincidence_count_rate = np.array(
-            [[4, 5, 6, 7, 8], [9, 10, 11, np.nan, 13], [14, 15, 16, 17, 18], [19, 20, 21, 22, 23]])
-        coincidence_count_rate_uncertainty = np.array(
-            [[0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5],
-             [0.1, 0.2, 0.3, 0.4, 0.5]])
-
-        chunk_of_five = SwapiL2Data(epoch, energy, coincidence_count_rate,
-                                    coincidence_count_rate_uncertainty)
-
-        input_metadata = InputMetadata(instrument, outgoing_data_level, start_date, end_date, input_version)
-
-        swapi_processor = SwapiProcessor(
-            Mock(), input_metadata)
-        with self.assertLogs(logger) as log_context:
-            product = swapi_processor.process_l3a_proton(data=chunk_of_five, dependencies=Mock())
-
-        self.assertIsInstance(product, SwapiL3ProtonSolarWindData)
-        np.testing.assert_array_equal(nominal_values(product.proton_sw_speed), [np.nan])
-        np.testing.assert_array_equal(std_devs(product.proton_sw_speed), [np.nan])
-
-        np.testing.assert_array_equal(nominal_values(product.proton_sw_temperature), [np.nan])
-        np.testing.assert_array_equal(std_devs(product.proton_sw_temperature), [np.nan])
-
-        np.testing.assert_array_equal(nominal_values(product.proton_sw_density), [np.nan])
-        np.testing.assert_array_equal(std_devs(product.proton_sw_density), [np.nan])
-
-        np.testing.assert_array_equal(nominal_values(product.proton_sw_clock_angle), [np.nan])
-        np.testing.assert_array_equal(std_devs(product.proton_sw_clock_angle), [np.nan])
-
-        np.testing.assert_array_equal(nominal_values(product.proton_sw_deflection_angle), [np.nan])
-        np.testing.assert_array_equal(std_devs(product.proton_sw_deflection_angle), [np.nan])
-
-        np.testing.assert_array_equal(product.quality_flags, [SwapiL3Flags.NONE])
-
-    def test_process_l3a_alpha_outputs_fill_for_chunks_with_fill(self):
-        instrument = 'swapi'
-        end_date = datetime(2025, 6, 13)
-        outgoing_data_level = "l3a"
-        start_date = datetime(2025, 6, 12)
-        input_version = "v123"
-        initial_epoch = 10
-
-        epoch = np.array([initial_epoch, 11, 12, 13])
-        energy = np.array([19000, 18000, 17000, 16000, 15000])
-        coincidence_count_rate = np.array(
-            [[4, 5, 6, 7, 8], [9, 10, 11, np.nan, 13], [14, 15, 16, 17, 18], [19, 20, 21, 22, 23]])
-        coincidence_count_rate_uncertainty = np.array(
-            [[0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5],
-             [0.1, 0.2, 0.3, 0.4, 0.5]])
-
-        chunk_of_five = SwapiL2Data(epoch, energy, coincidence_count_rate,
-                                    coincidence_count_rate_uncertainty)
-
-        input_metadata = InputMetadata(instrument, outgoing_data_level, start_date, end_date, input_version)
-
-        swapi_processor = SwapiProcessor(
-            Mock(), input_metadata)
-        with self.assertLogs(logger) as log_context:
-            product = swapi_processor.process_l3a_alpha_solar_wind(data=chunk_of_five, dependencies=Mock())
-
-        self.assertIsInstance(product, SwapiL3AlphaSolarWindData)
-        np.testing.assert_array_equal(nominal_values(product.alpha_sw_speed), [np.nan])
-        np.testing.assert_array_equal(std_devs(product.alpha_sw_speed), [np.nan])
-
-        np.testing.assert_array_equal(nominal_values(product.alpha_sw_temperature), [np.nan])
-        np.testing.assert_array_equal(std_devs(product.alpha_sw_temperature), [np.nan])
-
-        np.testing.assert_array_equal(nominal_values(product.alpha_sw_density), [np.nan])
-        np.testing.assert_array_equal(std_devs(product.alpha_sw_density), [np.nan])
-
-    def test_process_l3a_pui_outputs_fill_for_chunks_with_fill(self):
+    @patch('imap_l3_processing.swapi.swapi_processor.rotate_rtn_to_dps', return_value=np.array([np.nan, np.nan, np.nan]))
+    @patch('imap_l3_processing.swapi.swapi_processor.ParallelChunkRunner')
+    @patch('imap_l3_processing.swapi.swapi_processor.calculate_ten_minute_velocities')
+    def test_process_l3a_pui_outputs_fill_for_chunks_with_fill(self,
+                                                               mock_calculate_ten_minute_velocities,
+                                                               mock_parallel_chunk_runner_class,
+                                                               mock_rotate_rtn_to_dps):
         instrument = 'swapi'
         end_date = datetime(2025, 6, 13)
         outgoing_data_level = "l3a"
@@ -840,23 +418,31 @@ def test_process_l3a_pui_outputs_fill_for_chunks_with_fill(self):
         input_version = "v123"
         initial_epoch = 10
 
-        epoch = np.array([initial_epoch, 11, 12, 13])
-        energy = np.array([15000, 16000, 17000, 18000, 19000])
-        coincidence_count_rate = np.array(
-            [[4, 5, 6, 7, 8], [9, 10, 11, np.nan, 13], [14, 15, 16, 17, 18], [19, 20, 21, 22, 23]])
-        coincidence_count_rate_uncertainty = np.array(
-            [[0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5],
-             [0.1, 0.2, 0.3, 0.4, 0.5]])
-
-        chunk_of_five = SwapiL2Data(epoch, energy, coincidence_count_rate,
-                                    coincidence_count_rate_uncertainty)
+        epoch = np.arange(initial_epoch, initial_epoch + 50)
+        energy = np.tile(np.array([15000, 16000, 17000, 18000, 19000]), (50, 1))
+        coincidence_count_rate = np.full((50, 5), 5.0)
+        coincidence_count_rate[1, 3] = np.nan
+        coincidence_count_rate_uncertainty = np.full((50, 5), 0.1)
+
+        chunk_of_fifty = SwapiL2Data(epoch, energy, coincidence_count_rate,
+                                     coincidence_count_rate_uncertainty)
+
+        mock_runner = mock_parallel_chunk_runner_class.return_value
+        mock_runner.run.return_value = dict(
+            epoch=np.array([initial_epoch + THIRTY_SECONDS_IN_NANOSECONDS]),
+            proton_sw_bulk_velocity_rtn_sc=np.array([[np.nan, np.nan, np.nan]]),
+            quality_flags=np.array([SwapiL3Flags.NONE]),
+        )
+        mock_calculate_ten_minute_velocities.return_value = (
+            np.array([[np.nan, np.nan, np.nan]]),
+            np.array([SwapiL3Flags.NONE]),
+        )
 
         input_metadata = InputMetadata(instrument, outgoing_data_level, start_date, end_date, input_version)
 
-        swapi_processor = SwapiProcessor(
-            Mock(), input_metadata)
-        with self.assertLogs(logger) as log_context:
-            product = swapi_processor.process_l3a_pui(data=chunk_of_five, dependencies=Mock())
+        swapi_processor = SwapiProcessor(Mock(), input_metadata)
+        with self.assertLogs(logger):
+            product = swapi_processor.process_l3a_pui(data=chunk_of_fifty, dependencies=Mock())
 
         self.assertIsInstance(product, SwapiL3PickupIonData)
         np.testing.assert_array_equal(product.epoch, initial_epoch + FIVE_MINUTES_IN_NANOSECONDS)
@@ -884,19 +470,17 @@ def test_process_l3a_pui_outputs_fill_for_chunks_with_fill(self):
     @patch('imap_l3_processing.utils.ImapAttributeManager')
     @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3AlphaSolarWindData')
     @patch('imap_l3_processing.utils.write_cdf')
+    @patch('imap_l3_processing.swapi.swapi_processor.ParallelChunkRunner')
     @patch('imap_l3_processing.swapi.swapi_processor.chunk_l2_data')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_alpha_solar_wind_speed')
-    @patch(
-        'imap_l3_processing.swapi.swapi_processor.calculate_alpha_solar_wind_temperature_and_density_for_combined_sweeps')
     @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3ADependencies')
     @patch('imap_l3_processing.processor.spiceypy')
     def test_process_l3a_alpha(self, mock_spicepy,
-                               mock_swapi_l3_dependencies_class, mock_alpha_calculate_temperature_and_density,
-                               mock_calculate_alpha_solar_wind_speed,
-                               mock_chunk_l2_data, mock_write_cdf,
+                               mock_swapi_l3_dependencies_class,
+                               mock_chunk_l2_data,
+                               mock_parallel_chunk_runner_class,
+                               mock_write_cdf,
                                mock_alpha_solar_wind_data_constructor,
-                               mock_imap_attribute_manager
-                               ):
+                               mock_imap_attribute_manager):
         instrument = 'swapi'
         incoming_data_level = 'l2'
         dependency_start_date = datetime.strftime(datetime(2025, 1, 1), "%Y%m%d")
@@ -910,153 +494,104 @@ def test_process_l3a_alpha(self, mock_spicepy,
 
         mock_spicepy.ktotal.return_value = 0
 
-        returned_alpha_temperature = ufloat(400000, 2000)
-        returned_alpha_density = ufloat(0.15, 0.01)
-
-        returned_pre_lut_temp = ufloat(300000, 1000)
-        returned_pre_lut_density = ufloat(0.2, 0.02)
-
-        returned_bad_flags = SwapiL3Flags.NONE
-        mock_alpha_calculate_temperature_and_density.return_value = AlphaSolarWindTemperatureAndDensity(
-            returned_alpha_temperature, returned_alpha_density, returned_pre_lut_temp, returned_pre_lut_density, returned_bad_flags)
-
-        returned_alpha_speed = ufloat(450000, 1000)
-        mock_calculate_alpha_solar_wind_speed.return_value = ufloat(450000, 1000)
-
         initial_epoch = 10
-
         epoch = np.array([initial_epoch, 11, 12, 13])
         energy = np.array([15000, 16000, 17000, 18000, 19000])
         coincidence_count_rate = np.array(
             [[4, 5, 6, 7, 8], [9, 10, 11, 12, 13], [14, 15, 16, 17, 18], [19, 20, 21, 22, 23]])
-        coincidence_count_rate_uncertainty = np.array(
-            [[0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5],
-             [0.1, 0.2, 0.3, 0.4, 0.5]])
+        coincidence_count_rate_uncertainty = np.array([[0.1, 0.2, 0.3, 0.4, 0.5]] * 4)
 
         chunk_of_five = SwapiL2Data(epoch, energy, coincidence_count_rate,
                                     coincidence_count_rate_uncertainty)
 
+        runner_result = dict(
+            epoch=np.array([initial_epoch + THIRTY_SECONDS_IN_NANOSECONDS]),
+            alpha_sw_density=np.array([0.15]),
+            alpha_sw_density_uncert=np.array([0.01]),
+            alpha_sw_temperature=np.array([400000.0]),
+            alpha_sw_temperature_uncert=np.array([2000.0]),
+            alpha_sw_velocity_rtn=np.array([[450.0, 5.0, 1.0]]),
+            alpha_sw_velocity_covariance_rtn=np.array([np.eye(3)]),
+            alpha_sw_delta_v=np.array([12.0]),
+            alpha_sw_delta_v_uncert=np.array([1.0]),
+            alpha_sw_b_hat_rtn=np.array([[1.0, 0.0, 0.0]]),
+            quality_flags=np.array([int(SwapiL3Flags.NONE)]),
+        )
+        mock_runner = mock_parallel_chunk_runner_class.return_value
+        mock_runner.run.return_value = runner_result
+
         science_input = ScienceInput(
             f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf')
 
         input_file_names = [
             f'imap_{instrument}_{incoming_data_level}_{SWAPI_L2_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{PROTON_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{ALPHA_TEMPERATURE_DENSITY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{CLOCK_ANGLE_AND_FLOW_DEFLECTION_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{GEOMETRIC_FACTOR_SW_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f'imap_{instrument}_{EFFICIENCY_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
             f'imap_{instrument}_{INSTRUMENT_RESPONSE_LOOKUP_TABLE_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
-            f'imap_{instrument}_{DENSITY_OF_NEUTRAL_HELIUM_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f'imap_{instrument}_{AZIMUTHAL_TRANSMISSION_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f'imap_{instrument}_{CENTRAL_EFFECTIVE_AREA_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
+            f'imap_{instrument}_{PASSBAND_FIT_COEFFICIENTS_DESCRIPTOR}_{dependency_start_date}_{version}.cdf',
         ]
 
         ancillary_inputs = [AncillaryInput(file_name) for file_name in input_file_names[1:]]
-
         dependencies = ProcessingInputCollection(science_input, *ancillary_inputs)
 
         input_metadata = InputMetadata(instrument, outgoing_data_level, start_date, end_date, input_version)
 
         alpha_solar_wind_data = mock_alpha_solar_wind_data_constructor.return_value
-        expected_alpha_metadata = replace(input_metadata, descriptor='alpha-sw')
+        expected_alpha_metadata = replace(input_metadata, descriptor="alpha-sw")
         alpha_solar_wind_data.input_metadata = expected_alpha_metadata
 
-        descriptor_to_generate, expected_data_product = "alpha-sw", alpha_solar_wind_data
-
-        input_metadata.descriptor = descriptor_to_generate
+        input_metadata.descriptor = "alpha-sw"
 
         expected_cdf_path = (config["DATA_DIR"] / "imap" / "swapi" / "l3a" / "2025" / "08" /
-                             f"imap_swapi_l3a_{descriptor_to_generate}_{start_date_as_str}_{input_version}.cdf")
+                             f"imap_swapi_l3a_alpha-sw_{start_date_as_str}_{input_version}.cdf")
 
-        mock_chunk_l2_data.side_effect = [
-            [chunk_of_five],
-        ]
+        mock_chunk_l2_data.return_value = [chunk_of_five]
 
-        mock_swapi_l3_dependencies_class.fetch_dependencies.return_value.data = sentinel.swapi_l2_data
+        swapi_l3a_dependencies = create_swapi_l3a_dependencies_with_mocks()
+        swapi_l3a_dependencies.data = Mock(energy=energy)
+        swapi_l3a_dependencies.mag_data = Mock()
+        swapi_l3a_dependencies.mag_is_preliminary = False
+        mock_swapi_l3_dependencies_class.fetch_dependencies.return_value = swapi_l3a_dependencies
 
         mock_manager = mock_imap_attribute_manager.return_value
 
-        swapi_processor = SwapiProcessor(
-            dependencies, input_metadata)
+        swapi_processor = SwapiProcessor(dependencies, input_metadata)
         product = swapi_processor.process()
 
-        actual_science_input = swapi_processor.dependencies.get_science_inputs()[0]
-        self.assertEqual(actual_science_input.get_time_range()[0].strftime("%Y%m%d"), dependency_start_date)
-
         mock_swapi_l3_dependencies_class.fetch_dependencies.assert_called_once_with(dependencies)
-        swapi_l3a_dependencies = mock_swapi_l3_dependencies_class.fetch_dependencies.return_value
-        mock_alpha_temperature_density_calibration_table = swapi_l3a_dependencies.alpha_temperature_density_calibration_table
-        self.assertEqual(swapi_l3a_dependencies.efficiency_calibration_table.get_alpha_efficiency_for.return_value,
-                         mock_alpha_calculate_temperature_and_density.call_args_list[0].args[4])
-
-        swapi_l3a_dependencies.efficiency_calibration_table.get_alpha_efficiency_for.assert_called_once_with(
-            initial_epoch + THIRTY_SECONDS_IN_NANOSECONDS)
-
-        mock_chunk_l2_data.assert_has_calls([call(sentinel.swapi_l2_data, 5)])
-
-        expected_count_rate_with_uncertainties = uarray(coincidence_count_rate,
-                                                        coincidence_count_rate_uncertainty)
-
-        self.assertEqual(mock_alpha_temperature_density_calibration_table,
-                         mock_alpha_calculate_temperature_and_density.call_args_list[0].args[0])
-        self.assert_ufloat_equal(returned_alpha_speed,
-                                 mock_alpha_calculate_temperature_and_density.call_args_list[0].args[1])
-        np.testing.assert_array_equal(nominal_values(expected_count_rate_with_uncertainties),
-                                      nominal_values(
-                                          mock_alpha_calculate_temperature_and_density.call_args_list[0].args[
-                                              2]))
-        np.testing.assert_array_equal(std_devs(expected_count_rate_with_uncertainties),
-                                      std_devs(
-                                          mock_alpha_calculate_temperature_and_density.call_args_list[0].args[
-                                              2]))
-        np.testing.assert_array_equal(energy,
-                                      mock_alpha_calculate_temperature_and_density.call_args_list[0].args[3])
 
-        np.testing.assert_array_equal(nominal_values(expected_count_rate_with_uncertainties),
-                                      nominal_values(
-                                          mock_calculate_alpha_solar_wind_speed.call_args_list[0].args[0]))
-        np.testing.assert_array_equal(std_devs(expected_count_rate_with_uncertainties),
-                                      std_devs(mock_calculate_alpha_solar_wind_speed.call_args_list[0].args[0]))
-        np.testing.assert_array_equal(energy, mock_calculate_alpha_solar_wind_speed.call_args_list[0].args[1])
-
-        mock_manager.add_global_attribute.assert_has_calls([call("Data_version", outgoing_version),
-                                                            call("Generation_date",
-                                                                 date.today().strftime("%Y%m%d")),
-                                                            call("Logical_source",
-                                                                 f"imap_swapi_l3a_{descriptor_to_generate}"),
-                                                            call("Logical_file_id",
-                                                                 f"imap_swapi_l3a_{descriptor_to_generate}_{start_date_as_str}_{input_version}"),
-                                                            ])
-
-        actual_alpha_metadata, actual_alpha_epoch, actual_alpha_sw_speed, actual_alpha_sw_temperature, actual_alpha_sw_density, actual_bad_fit_flag,\
-            actual_pre_lut_temp, actual_pre_lut_density= mock_alpha_solar_wind_data_constructor.call_args.args
-        self.assertEqual(expected_alpha_metadata, actual_alpha_metadata)
-
-        np.testing.assert_array_equal(np.array([initial_epoch + THIRTY_SECONDS_IN_NANOSECONDS]),
-                                      actual_alpha_epoch)
-        np.testing.assert_array_equal(np.array([mock_calculate_alpha_solar_wind_speed.return_value]),
-                                      actual_alpha_sw_speed)
-        np.testing.assert_array_equal(np.array([mock_alpha_calculate_temperature_and_density.return_value.temperature]),
-                                      actual_alpha_sw_temperature)
-        np.testing.assert_array_equal(np.array([mock_alpha_calculate_temperature_and_density.return_value.density]),
-                                      actual_alpha_sw_density)
-        np.testing.assert_array_equal(
-            np.array([mock_alpha_calculate_temperature_and_density.return_value.bad_fit_flag]),
-            actual_bad_fit_flag)
-
-        np.testing.assert_array_equal(np.array([mock_alpha_calculate_temperature_and_density.return_value.pre_lut_temperature]),
-                                      actual_pre_lut_temp)
-        np.testing.assert_array_equal(np.array([mock_alpha_calculate_temperature_and_density.return_value.pre_lut_density]),
-                                      actual_pre_lut_density)
-
-        mock_manager.add_instrument_attrs.assert_called_once_with("swapi", "l3a", descriptor_to_generate)
+        swapi_l3a_dependencies.swapi_response.warm_cache.assert_called_once()
+
+        mock_parallel_chunk_runner_class.assert_called_once_with(
+            swapi_l3a_dependencies.swapi_response,
+            swapi_l3a_dependencies.efficiency_calibration_table,
+        )
+        mock_runner.run.assert_called_once()
+        actual_chunks = mock_runner.run.call_args.args[0]
+        self.assertEqual([chunk_of_five], actual_chunks)
+
+        actual_kwargs = mock_alpha_solar_wind_data_constructor.call_args.kwargs
+        actual_positional = mock_alpha_solar_wind_data_constructor.call_args.args
+        self.assertEqual(expected_alpha_metadata, actual_positional[0])
+        for key, expected_val in runner_result.items():
+            np.testing.assert_array_equal(expected_val, actual_kwargs[key])
+
+        mock_manager.add_global_attribute.assert_has_calls([
+            call("Data_version", outgoing_version),
+            call("Generation_date", date.today().strftime("%Y%m%d")),
+            call("Logical_source", "imap_swapi_l3a_alpha-sw"),
+            call("Logical_file_id", f"imap_swapi_l3a_alpha-sw_{start_date_as_str}_{input_version}"),
+        ])
+        mock_manager.add_instrument_attrs.assert_called_once_with("swapi", "l3a", "alpha-sw")
 
-        self.assertEqual(input_file_names, expected_data_product.parent_file_names)
-        mock_write_cdf.assert_called_once_with(str(expected_cdf_path), expected_data_product, mock_manager)
+        self.assertEqual(input_file_names, alpha_solar_wind_data.parent_file_names)
+        mock_write_cdf.assert_called_once_with(str(expected_cdf_path), alpha_solar_wind_data, mock_manager)
         self.assertEqual([expected_cdf_path], product)
 
     @patch('imap_l3_processing.swapi.swapi_processor.calculate_delta_minus_plus')
     @patch('imap_l3_processing.swapi.swapi_processor.save_data')
     @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3BCombinedVDF')
-    @patch('imap_l3_processing.swapi.swapi_processor.calculate_combined_sweeps')
     @patch('imap_l3_processing.swapi.swapi_processor.chunk_l2_data')
     @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3BDependencies')
     @patch('imap_l3_processing.swapi.swapi_processor.calculate_alpha_solar_wind_vdf')
@@ -1070,7 +605,7 @@ def test_process_l3b(self, mock_spiceypy, mock_calculate_combined_solar_wind_dif
                          mock_calculate_alpha_solar_wind_vdf,
                          mock_swapi_l3b_dependencies_class,
                          mock_chunk_l2_data,
-                         mock_calculate_combined_sweeps, mock_combined_vdf_data,
+                         mock_combined_vdf_data,
                          mock_save_data,
                          mock_calculate_delta_minus_plus):
         instrument = 'swapi'
@@ -1114,31 +649,36 @@ def test_process_l3b(self, mock_spiceypy, mock_calculate_combined_solar_wind_dif
             DeltaMinusPlus(sentinel.energy_delta_minus2, sentinel.energy_delta_plus2),
         ]
 
-        energy = np.array([15000, 16000, 17000, 18000, 19000])
-        coincidence_count_rate = np.array(
-            [[4, 5, 6, 7, 8], [9, 10, 11, 12, 13], [14, 15, 16, 17, 18], [19, 20, 21, 22, 23]])
-        average_coincident_count_rates = [14, 15, 16, 17, 18]
+        n_sweeps, n_bins = 4, 72
+        coarse_count_rates = np.linspace(14.0, 75.0, 62)
+        coarse_count_rate_uncertainties = np.linspace(0.1, 6.2, 62)
+        coarse_energies = np.linspace(15000.0, 76000.0, 62)
 
-        coincidence_count_rate_uncertainty = np.array(
-            [[0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5], [0.1, 0.2, 0.3, 0.4, 0.5],
-             [0.1, 0.2, 0.3, 0.4, 0.5]])
-        average_coincident_count_rate_uncertainties = [0.1, 0.2, 0.3, 0.4, 0.5]
+        # All sweeps identical so the per-bin mean across sweeps equals the per-bin pattern.
+        coincidence_count_rate = np.zeros((n_sweeps, n_bins))
+        coincidence_count_rate[:, 1:63] = coarse_count_rates
+        coincidence_count_rate_uncertainty = np.zeros((n_sweeps, n_bins))
+        coincidence_count_rate_uncertainty[:, 1:63] = coarse_count_rate_uncertainties
+        energy_per_sweep = np.zeros((n_sweeps, n_bins))
+        energy_per_sweep[:, 1:63] = coarse_energies
+
+        average_coincident_count_rates = coarse_count_rates
+        # σ propagation through Σ/N over n independent sweeps shrinks the per-sweep σ by √n.
+        average_coincident_count_rate_uncertainties = coarse_count_rate_uncertainties / np.sqrt(n_sweeps)
+        energy = coarse_energies
 
         first_chunk_initial_epoch = 10
-        first_l2_data_chunk = SwapiL2Data(np.array([first_chunk_initial_epoch, 11, 12, 13]), sentinel.energies,
+        first_l2_data_chunk = SwapiL2Data(np.array([first_chunk_initial_epoch, 11, 12, 13]), energy_per_sweep,
                                           coincidence_count_rate,
                                           coincidence_count_rate_uncertainty)
 
         second_chunk_initial_epoch = 60
-        second_l2_data_chunk = SwapiL2Data(np.array([second_chunk_initial_epoch, 11, 12, 13]), sentinel.energies,
+        second_l2_data_chunk = SwapiL2Data(np.array([second_chunk_initial_epoch, 11, 12, 13]), energy_per_sweep,
                                            coincidence_count_rate,
                                            coincidence_count_rate_uncertainty)
 
         mock_chunk_l2_data.return_value = [first_l2_data_chunk, second_l2_data_chunk]
 
-        mock_calculate_combined_sweeps.return_value = [
-            uarray(average_coincident_count_rates, average_coincident_count_rate_uncertainties), energy]
-
         input_metadata = InputMetadata(instrument, outgoing_data_level, start_date, end_date,
                                        outgoing_version)
 
@@ -1164,12 +704,6 @@ def test_process_l3b(self, mock_spiceypy, mock_calculate_combined_solar_wind_dif
         mock_chunk_l2_data.assert_called_with(mock_swapi_l3b_dependencies_class.fetch_dependencies.return_value.data,
                                               50)
 
-        np.testing.assert_array_equal(coincidence_count_rate,
-                                      nominal_values(mock_calculate_combined_sweeps.call_args_list[0].args[0]))
-        np.testing.assert_array_equal(coincidence_count_rate_uncertainty,
-                                      std_devs(mock_calculate_combined_sweeps.call_args_list[0].args[0]))
-        self.assertEqual(sentinel.energies,
-                         mock_calculate_combined_sweeps.call_args_list[0].args[1])
         mock_efficiency_table.get_proton_efficiency_for.assert_has_calls(
             [call(first_chunk_initial_epoch + FIVE_MINUTES_IN_NANOSECONDS),
              call(second_chunk_initial_epoch + FIVE_MINUTES_IN_NANOSECONDS)])
@@ -1221,16 +755,15 @@ def test_process_l3b(self, mock_spiceypy, mock_calculate_combined_solar_wind_dif
                          mock_calculate_combined_solar_wind_differential_flux.call_args_list[0].args[2])
         self.assertEqual(mock_geometric_factor_calibration_table,
                          mock_calculate_combined_solar_wind_differential_flux.call_args_list[0].args[3])
-        mock_calculate_delta_minus_plus.assert_has_calls([
-            call(sentinel.proton_calculated_velocities1),
-            call(sentinel.alpha_calculated_velocities1),
-            call(sentinel.pui_calculated_velocities1),
-            call(energy),
-            call(sentinel.proton_calculated_velocities2),
-            call(sentinel.alpha_calculated_velocities2),
-            call(sentinel.pui_calculated_velocities2),
-            call(energy),
-        ])
+        delta_calls = mock_calculate_delta_minus_plus.call_args_list
+        self.assertEqual(sentinel.proton_calculated_velocities1, delta_calls[0].args[0])
+        self.assertEqual(sentinel.alpha_calculated_velocities1, delta_calls[1].args[0])
+        self.assertEqual(sentinel.pui_calculated_velocities1, delta_calls[2].args[0])
+        np.testing.assert_array_equal(energy, delta_calls[3].args[0])
+        self.assertEqual(sentinel.proton_calculated_velocities2, delta_calls[4].args[0])
+        self.assertEqual(sentinel.alpha_calculated_velocities2, delta_calls[5].args[0])
+        self.assertEqual(sentinel.pui_calculated_velocities2, delta_calls[6].args[0])
+        np.testing.assert_array_equal(energy, delta_calls[7].args[0])
 
         expected_combined_metadata = InputMetadata(descriptor="combined", data_level=outgoing_data_level,
                                                    start_date=start_date, end_date=end_date, instrument=instrument,
@@ -1292,41 +825,43 @@ def test_process_l3b(self, mock_spiceypy, mock_calculate_combined_solar_wind_dif
         mock_save_data.assert_called_once_with(mock_combined_vdf_data.return_value)
         self.assertEqual([mock_save_data.return_value], product)
 
-    def assert_ufloat_equal(self, expected_ufloat, actual_ufloat):
-        self.assertEqual(expected_ufloat.n, actual_ufloat.n)
-        self.assertEqual(expected_ufloat.s, actual_ufloat.s)
 
-    def compare_ufloat_equal(self, expected_ufloat, actual_ufloat):
-        try:
-            self.assertEqual(expected_ufloat.n, actual_ufloat.n)
-            self.assertEqual(expected_ufloat.s, actual_ufloat.s)
-            return True
-        except AssertionError:
-            return False
+    @patch('imap_l3_processing.swapi.swapi_processor.SwapiL3ADependencies')
+    @patch('imap_l3_processing.processor.spiceypy')
+    def test_process_l3a_raises_for_unknown_descriptor(self, mock_spicepy,
+                                                       mock_swapi_l3_dependencies_class):
+        mock_spicepy.ktotal.return_value = 0
+        mock_swapi_l3_dependencies_class.fetch_dependencies.return_value = create_swapi_l3a_dependencies_with_mocks()
+
+        input_metadata = InputMetadata('swapi', 'l3a', datetime(2025, 6, 12), datetime(2025, 6, 13), 'v123')
+        input_metadata.descriptor = "not-a-real-descriptor"
+
+        swapi_processor = SwapiProcessor(Mock(), input_metadata)
+        with self.assertRaises(NotImplementedError) as cm:
+            swapi_processor.process()
+        self.assertEqual(("unknown descriptor", "not-a-real-descriptor"), cm.exception.args)
+
+    def test_process_l3a_alpha_raises_when_mag_data_missing(self):
+        input_metadata = InputMetadata('swapi', 'l3a', datetime(2025, 8, 28), datetime(2025, 8, 29), 'v123')
+        input_metadata.descriptor = "alpha-sw"
 
-    def assert_ufloat_array(self, expected_array, actual_array):
-        self.assertTrue(all([self.compare_ufloat_equal(expected, actual) for expected, actual in
-                             zip(expected_array, actual_array)]))
+        dependencies = create_swapi_l3a_dependencies_with_mocks()
+        dependencies.mag_data = None
 
+        swapi_processor = SwapiProcessor(Mock(), input_metadata)
+        with self.assertRaises(ValueError) as cm:
+            swapi_processor.process_l3a_alpha(dependencies.data, dependencies)
+        self.assertIn("alpha-sw requires MAG RTN data", str(cm.exception))
 
+        
 def create_swapi_l3a_dependencies_with_mocks():
-    data = Mock()
-    proton_temperature_density_calibration_table = Mock()
-    alpha_temperature_density_calibration_table = Mock()
-    clock_angle_and_flow_deflection_calibration_table = Mock()
-    efficiency_calibration_table = Mock()
-    geometric_factor_calibration_table = Mock()
-    instrument_response_calibration_table = Mock()
-    density_of_neutral_helium_calibration_table = Mock()
     return SwapiL3ADependencies(
-        data=data,
-        proton_temperature_density_calibration_table=proton_temperature_density_calibration_table,
-        alpha_temperature_density_calibration_table=alpha_temperature_density_calibration_table,
-        clock_angle_and_flow_deflection_calibration_table=clock_angle_and_flow_deflection_calibration_table,
-        efficiency_calibration_table=efficiency_calibration_table,
-        geometric_factor_calibration_table=geometric_factor_calibration_table,
-        instrument_response_calibration_table=instrument_response_calibration_table,
-        density_of_neutral_helium_calibration_table=density_of_neutral_helium_calibration_table,
+        data=Mock(),
+        efficiency_calibration_table=Mock(),
+        geometric_factor_calibration_table=Mock(),
+        instrument_response_calibration_table=Mock(),
+        density_of_neutral_helium_calibration_table=Mock(),
         hydrogen_inflow_vector=Mock(),
         helium_inflow_vector=Mock(),
+        swapi_response=Mock(),
     )
diff --git a/tests/swe/l3/test_swe_l3_dependencies.py b/tests/swe/l3/test_swe_l3_dependencies.py
index c29f0dbc9..18aa9f3a3 100644
--- a/tests/swe/l3/test_swe_l3_dependencies.py
+++ b/tests/swe/l3/test_swe_l3_dependencies.py
@@ -43,9 +43,10 @@ def test_from_file_paths(self, mock_read_swe_config, mock_read_swapi_data, mock_
         )
         self.assertTrue(result_preliminary.mag_is_preliminary)
 
+    @patch("imap_l3_processing.utils.download")
     @patch(f"{MODULE}.download")
     @patch(f"{MODULE}.SweL3Dependencies.from_file_paths")
-    def test_fetch_dependencies(self, mock_from_file_paths, mock_download_dependency):
+    def test_fetch_dependencies(self, mock_from_file_paths, mock_download_dependency, mock_utils_download):
         swe_l2_dependency = ScienceInput("imap_swe_l2_sci_20200101_v000.cdf")
         swe_l1b_dependency = ScienceInput("imap_swe_l1b_sci_20200101_v000.cdf")
         mag_l1d_dependency = ScienceInput("imap_mag_l1d_norm-dsrf_20200101_v000.cdf")
@@ -61,18 +62,19 @@ def test_fetch_dependencies(self, mock_from_file_paths, mock_download_dependency
         expected_mag_path = Mock()
         expected_swapi_path = Mock()
         expected_config_path = Mock()
-        mock_download_dependency.side_effect = [expected_swe_path, expected_swe_l1b_path, expected_mag_path,
+        mock_download_dependency.side_effect = [expected_swe_path, expected_swe_l1b_path,
                                                 expected_swapi_path,
                                                 expected_config_path]
+        mock_utils_download.return_value = expected_mag_path
 
         result = SweL3Dependencies.fetch_dependencies(processing_input_collection)
 
         mock_download_dependency.assert_has_calls([call(swe_l2_dependency.imap_file_paths[0].construct_path()),
                                                    call(swe_l1b_dependency.imap_file_paths[0].construct_path()),
-                                                   call(mag_l1d_dependency.imap_file_paths[0].construct_path()),
                                                    call(swapi_l3a_dependency.imap_file_paths[0].construct_path()),
                                                    call(config_dependency.imap_file_paths[0].construct_path())],
                                                   any_order=False)
+        mock_utils_download.assert_called_once_with(mag_l1d_dependency.imap_file_paths[0].construct_path())
 
         mock_from_file_paths.assert_called_with(expected_swe_path, expected_swe_l1b_path, expected_mag_path,
                                                 expected_swapi_path,
@@ -80,9 +82,27 @@ def test_fetch_dependencies(self, mock_from_file_paths, mock_download_dependency
                                                 mag_is_preliminary=True)
         self.assertEqual(mock_from_file_paths.return_value, result)
 
+    @patch(f"{MODULE}.select_mag_path")
+    @patch(f"{MODULE}.download")
+    def test_fetch_dependencies_raises_if_no_mag(self, _, mock_select_mag_path):
+        swe_l2_dependency = ScienceInput("imap_swe_l2_sci_20200101_v000.cdf")
+        swe_l1b_dependency = ScienceInput("imap_swe_l1b_sci_20200101_v000.cdf")
+        swapi_l3a_dependency = ScienceInput("imap_swapi_l3_proton-sw_20200101_v000.cdf")
+        config_dependency = AncillaryInput("imap_swe_config_20250101_v000.json")
+        processing_input_collection = ProcessingInputCollection(
+            swe_l2_dependency, swe_l1b_dependency, swapi_l3a_dependency, config_dependency,
+        )
+        mock_select_mag_path.return_value = (None, None)
+
+        with self.assertRaises(ValueError) as cm:
+            SweL3Dependencies.fetch_dependencies(processing_input_collection)
+
+        self.assertIn("norm-dsrf", str(cm.exception))
+
+    @patch("imap_l3_processing.utils.download")
     @patch(f"{MODULE}.download")
     @patch(f"{MODULE}.SweL3Dependencies.from_file_paths")
-    def test_fetch_dependencies_prioritizes_mag_l2_data(self, mock_from_file_paths, mock_download):
+    def test_fetch_dependencies_prioritizes_mag_l2_data(self, mock_from_file_paths, mock_download, mock_utils_download):
         swe_l2_dependency = ScienceInput("imap_swe_l2_sci_20200101_v000.cdf")
         swe_l1b_dependency = ScienceInput("imap_swe_l1b_sci_20200101_v000.cdf")
         mag_l1d_dependency = ScienceInput("imap_mag_l1d_norm-dsrf_20200101_v000.cdf")
@@ -107,19 +127,19 @@ def test_fetch_dependencies_prioritizes_mag_l2_data(self, mock_from_file_paths,
         mock_download.side_effect = [
             expected_swe_path,
             expected_swe_l1b_path,
-            expected_mag_path,
             expected_swapi_path,
             expected_config_path,
         ]
+        mock_utils_download.return_value = expected_mag_path
 
         result = SweL3Dependencies.fetch_dependencies(processing_input_collection)
 
         mock_download.assert_has_calls([call(swe_l2_dependency.imap_file_paths[0].construct_path()),
                                         call(swe_l1b_dependency.imap_file_paths[0].construct_path()),
-                                        call(mag_l2_dependency.imap_file_paths[0].construct_path()),
                                         call(swapi_l3a_dependency.imap_file_paths[0].construct_path()),
                                         call(config_dependency.imap_file_paths[0].construct_path())],
                                        any_order=False)
+        mock_utils_download.assert_called_once_with(mag_l2_dependency.imap_file_paths[0].construct_path())
 
         mock_from_file_paths.assert_called_with(
             expected_swe_path,
diff --git a/tests/swe/l3/test_utils.py b/tests/swe/l3/test_utils.py
index e0d230c69..5391f2a07 100644
--- a/tests/swe/l3/test_utils.py
+++ b/tests/swe/l3/test_utils.py
@@ -15,6 +15,25 @@
 from tests.test_helpers import get_test_data_path
 
 
+def _copy_with_rtn_velocity(source: Path, dest: Path) -> Path:
+    """Copy a SWAPI L3a CDF and inject `proton_sw_bulk_velocity_rtn_sc`.
+
+    The committed test fixtures predate the variable; tests build an augmented
+    copy in a tempdir rather than mutating the checked-in CDF."""
+    shutil.copyfile(source, dest)
+    with CDF(str(dest), readonly=False) as cdf:
+        if 'proton_sw_bulk_velocity_rtn_sc' in cdf:
+            return dest
+        speed = cdf['proton_sw_speed'][:].astype(float)
+        velocity_rtn = np.stack(
+            [-speed, np.zeros_like(speed), np.zeros_like(speed)], axis=-1
+        ).astype(np.float32)
+        cdf.new('proton_sw_bulk_velocity_rtn_sc', data=velocity_rtn)
+        cdf['proton_sw_bulk_velocity_rtn_sc'].attrs['FILLVAL'] = np.float32(-1e31)
+        cdf['proton_sw_bulk_velocity_rtn_sc'].attrs['UNITS'] = 'km/s'
+    return dest
+
+
 class TestUtils(SpiceTestCase):
     def test_read_swe_config(self):
         result = read_swe_config(get_test_data_path('swe/example_swe_config.json'))
@@ -88,39 +107,35 @@ def test_read_l2_swe_data(self):
         self.assertEqual(result.acquisition_duration[0][0][0], 80000)
 
     def test_read_l3a_swapi_proton_data(self):
-        result = read_l3a_swapi_proton_data(get_test_data_path('swe/imap_swapi_l3a_proton-sw_20250101_v001.cdf'))
+        with tempfile.TemporaryDirectory() as tempdir:
+            cdf_path = _copy_with_rtn_velocity(
+                get_test_data_path('swe/imap_swapi_l3a_proton-sw_20250101_v001.cdf'),
+                Path(tempdir, 'swapi_l3a_with_rtn.cdf'),
+            )
+            result = read_l3a_swapi_proton_data(cdf_path)
         self.assertIsInstance(result, SwapiL3aProtonData)
         self.assertEqual(10, len(result.epoch))
         self.assertEqual(10, len(result.epoch_delta))
-        self.assertEqual(10, len(result.proton_sw_speed))
-        self.assertEqual(10, len(result.proton_sw_clock_angle))
-        self.assertEqual(10, len(result.proton_sw_deflection_angle))
+        self.assertEqual((10, 3), result.proton_sw_velocity_rtn.shape)
         self.assertEqual(10, len(result.swp_flags))
         self.assertEqual(datetime(2025, 1, 1), result.epoch[0])
         self.assertEqual(timedelta(seconds=30), result.epoch_delta[0])
-        self.assertEqual(498.4245091006667, result.proton_sw_speed[0])
-        self.assertEqual(82.53712019721974, result.proton_sw_clock_angle[0])
-        self.assertEqual(5.553957246800335e-06, result.proton_sw_deflection_angle[0])
         np.testing.assert_array_equal(
             np.array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.uint16), result.swp_flags, strict=True
         )
 
     def test_read_l3a_swapi_proton_data_with_fill_values(self):
         with tempfile.TemporaryDirectory() as tempdir:
-            cdf_with_fill_path = Path(tempdir, 'swe_file_with_fill.cdf')
-            shutil.copyfile(get_test_data_path('swe/imap_swapi_l3a_proton-sw_20250101_v001.cdf'), cdf_with_fill_path)
+            cdf_with_fill_path = _copy_with_rtn_velocity(
+                get_test_data_path('swe/imap_swapi_l3a_proton-sw_20250101_v001.cdf'),
+                Path(tempdir, 'swe_file_with_fill.cdf'),
+            )
             with CDF(str(cdf_with_fill_path), readonly=False) as cdf:
-                proton_sw_speed_fill_value = cdf['proton_sw_speed'].attrs['FILLVAL']
-                proton_sw_clock_angle_fill_value = cdf['proton_sw_clock_angle'].attrs['FILLVAL']
-                proton_sw_deflection_angle_fill_value = cdf['proton_sw_deflection_angle'].attrs['FILLVAL']
-                cdf['proton_sw_speed'][0] = proton_sw_speed_fill_value
-                cdf['proton_sw_clock_angle'][0] = proton_sw_clock_angle_fill_value
-                cdf['proton_sw_deflection_angle'][0] = proton_sw_deflection_angle_fill_value
+                velocity_fill_value = cdf['proton_sw_bulk_velocity_rtn_sc'].attrs['FILLVAL']
+                cdf['proton_sw_bulk_velocity_rtn_sc'][0] = [velocity_fill_value] * 3
 
             swapi_l3a_data = read_l3a_swapi_proton_data(cdf_with_fill_path)
-            self.assertTrue(np.isnan(swapi_l3a_data.proton_sw_speed[0]))
-            self.assertTrue(np.isnan(swapi_l3a_data.proton_sw_clock_angle[0]))
-            self.assertTrue(np.isnan(swapi_l3a_data.proton_sw_deflection_angle[0]))
+            self.assertTrue(np.all(np.isnan(swapi_l3a_data.proton_sw_velocity_rtn[0])))
 
     def test_compute_epoch_delta_in_ns(self):
         acq_duration_microseconds = np.full((4, 24, 30), 80_000)
diff --git a/tests/swe/test_swe_processor.py b/tests/swe/test_swe_processor.py
index 5baacb01d..19b3098b6 100644
--- a/tests/swe/test_swe_processor.py
+++ b/tests/swe/test_swe_processor.py
@@ -7,7 +7,6 @@
 from imap_data_access.processing_input import ScienceInput, ProcessingInputCollection, AncillaryInput
 
 from imap_l3_processing.models import MagData, InputMetadata
-from imap_l3_processing.swapi.quality_flags import SwapiL3Flags
 from imap_l3_processing.swe.l3.models import SweL2Data, SwapiL3aProtonData, SweL1bData
 from imap_l3_processing.swe.l3.models import SweL3MomentData
 from imap_l3_processing.swe.l3.science.moment_calculations import MomentFitResults, ScaleDensityOutput
@@ -106,9 +105,8 @@ def test_calculate_products(self, mock_calculate_moment_products, mock_calculate
         swapi_l3a_proton_data = SwapiL3aProtonData(
             epoch=swapi_epochs,
             epoch_delta=np.repeat(timedelta(seconds=30), 10),
-            proton_sw_speed=np.array([]),
-            proton_sw_clock_angle=np.array([]),
-            proton_sw_deflection_angle=np.array([]),
+            proton_sw_velocity_rtn=np.zeros((0, 3)),
+            proton_sw_speed=np.zeros(0),
             swp_flags=np.array([]),
         )
         mock_average_over_look_directions.return_value = np.array([5, 10, 15])
@@ -272,7 +270,7 @@ def test_calculate_products(self, mock_calculate_moment_products, mock_calculate
     @patch('imap_l3_processing.swe.swe_processor.calculate_velocity_in_dsp_frame_km_s')
     @patch('imap_l3_processing.swe.swe_processor.average_over_look_directions')
     @patch('imap_l3_processing.swe.swe_processor.mec_breakpoint_finder')
-    @patch('imap_l3_processing.swe.swe_processor.calculate_solar_wind_velocity_vector')
+    @patch('imap_l3_processing.swe.swe_processor.rotate_rtn_vectors_to_dps')
     @patch('imap_l3_processing.swe.swe_processor.correct_and_rebin')
     @patch('imap_l3_processing.swe.swe_processor.integrate_distribution_to_get_1d_spectrum')
     @patch('imap_l3_processing.swe.swe_processor.integrate_distribution_to_get_inbound_and_outbound_1d_spectrum')
@@ -281,7 +279,7 @@ def test_calculate_pitch_angle_and_gyrophase_products(self, mock_find_closest_ne
                                                           mock_integrate_distribution_to_get_inbound_and_outbound_1d_spectrum,
                                                           mock_integrate_distribution_to_get_1d_spectrum,
                                                           mock_correct_and_rebin,
-                                                          mock_calculate_solar_wind_velocity_vector,
+                                                          mock_rotate_rtn_vectors_to_dps,
                                                           _,
                                                           mock_average_over_look_directions,
                                                           mock_calculate_velocities, mock_swe_rebin_intensity):
@@ -322,11 +320,14 @@ def test_calculate_pitch_angle_and_gyrophase_products(self, mock_find_closest_ne
         swapi_l3a_proton_data = SwapiL3aProtonData(
             epoch=swapi_epochs,
             epoch_delta=np.repeat(timedelta(seconds=30), 10),
-            proton_sw_speed=np.array([]),
-            proton_sw_clock_angle=np.array([]),
-            proton_sw_deflection_angle=np.array([]),
-            swp_flags=np.array([0,0,0,SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED, SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED | SwapiL3Flags.HI_CHI_SQ, SwapiL3Flags.HI_CHI_SQ,0,0,0,0]),
+            proton_sw_velocity_rtn=np.arange(30).reshape(10, 3).astype(float),
+            proton_sw_speed=np.full(10, 400.0),
+            swp_flags=np.zeros(10),
         )
+        rotated_dps_vectors = np.arange(30).reshape(10, 3).astype(float)
+        rotated_dps_vectors[3] = np.nan
+        rotated_dps_vectors[4] = np.nan
+        mock_rotate_rtn_vectors_to_dps.return_value = rotated_dps_vectors
         counts = swe_l1b_data.count_rates * swe_l2_data.acquisition_duration[:, :, np.newaxis] / 1e6
         mock_average_over_look_directions.return_value = np.array([5, 10, 15])
         closest_mag_data = np.arange(9).reshape(3, 3)
@@ -400,10 +401,13 @@ def test_calculate_pitch_angle_and_gyrophase_products(self, mock_find_closest_ne
 
         self.assertEqual(3, mock_correct_and_rebin.call_count)
         self.assertEqual(3, mock_integrate_distribution_to_get_1d_spectrum.call_count)
-        mock_calculate_solar_wind_velocity_vector.assert_called_once_with(
-            swel3_dependency.swapi_l3a_proton_data.proton_sw_speed,
-            swel3_dependency.swapi_l3a_proton_data.proton_sw_clock_angle,
-            swel3_dependency.swapi_l3a_proton_data.proton_sw_deflection_angle)
+        mock_rotate_rtn_vectors_to_dps.assert_called_once()
+        rotate_args = mock_rotate_rtn_vectors_to_dps.call_args[0]
+        np.testing.assert_array_equal(rotate_args[0], swapi_epochs)
+        np.testing.assert_array_equal(rotate_args[1], swapi_l3a_proton_data.proton_sw_velocity_rtn)
+        expected_dps_vectors_after_speed_fallback = rotated_dps_vectors.copy()
+        expected_dps_vectors_after_speed_fallback[3] = [0.0, 0.0, -400.0]
+        expected_dps_vectors_after_speed_fallback[4] = [0.0, 0.0, -400.0]
         mock_find_closest_neighbor.assert_has_calls([
             call(
                 from_epoch=mag_epochs,
@@ -413,7 +417,7 @@ def test_calculate_pitch_angle_and_gyrophase_products(self, mock_find_closest_ne
             ),
             call(
                 from_epoch=swapi_epochs,
-                from_data=mock_calculate_solar_wind_velocity_vector.return_value,
+                from_data=NumpyArrayMatcher(expected_dps_vectors_after_speed_fallback),
                 to_epoch=epochs,
                 maximum_distance=np.timedelta64(5, 'm')
             )
@@ -484,8 +488,9 @@ def call_with_array_matchers(*args):
             call(rebinned_by_pitch_list[2], swe_config)
         ])
 
+    @patch("imap_l3_processing.swe.swe_processor.rotate_rtn_vectors_to_dps")
     @patch("imap_l3_processing.swe.swe_processor.SweProcessor.calculate_moment_products")
-    def test_calculate_pitch_angle_products_makes_nan_if_no_mag_close_enough(self, mock_calculate_moments):
+    def test_calculate_pitch_angle_products_makes_nan_if_no_mag_close_enough(self, mock_calculate_moments, mock_rotate_rtn_vectors_to_dps):
         epochs = np.array([datetime(2025, 3, 6)])
         mag_epochs = np.array([
             datetime(2025, 3, 6, 0, 1, 30),
@@ -500,6 +505,7 @@ def test_calculate_pitch_angle_products_makes_nan_if_no_mag_close_enough(self, m
             datetime(2025, 3, 6, 0, 10, 30),
         ])
         swapi_epochs = np.array([datetime(2025, 3, 6)])
+        mock_rotate_rtn_vectors_to_dps.return_value = np.tile([0.0, 0.0, -400.0], (len(swapi_epochs), 1))
 
         pitch_angle_bins = [70, 100, 130]
 
@@ -537,9 +543,8 @@ def test_calculate_pitch_angle_products_makes_nan_if_no_mag_close_enough(self, m
         swapi_l3a_proton_data = SwapiL3aProtonData(
             epoch=swapi_epochs,
             epoch_delta=np.repeat(timedelta(seconds=30), 10),
-            proton_sw_speed=np.full(len(swapi_epochs), 400),
-            proton_sw_clock_angle=np.full(len(swapi_epochs), 0),
-            proton_sw_deflection_angle=np.full(len(swapi_epochs), 0),
+            proton_sw_velocity_rtn=np.tile([-400.0, 0.0, 0.0], (len(swapi_epochs), 1)),
+            proton_sw_speed=np.full(len(swapi_epochs), 400.0),
             swp_flags=np.full(len(swapi_epochs), 0)
         )
         geometric_fractions = [0.0697327, 0.138312, 0.175125, 0.181759,
@@ -592,14 +597,16 @@ def test_calculate_pitch_angle_products_makes_nan_if_no_mag_close_enough(self, m
                                       np.full((len(epochs), num_energies, len(pitch_angle_bins), len(gyrophase_bins)),
                                               np.nan))
 
+    @patch("imap_l3_processing.swe.swe_processor.rotate_rtn_vectors_to_dps")
     @patch("imap_l3_processing.swe.swe_processor.SweProcessor.calculate_moment_products")
-    def test_calculate_pitch_angle_products_without_mocks(self, mock_calculate_moments):
+    def test_calculate_pitch_angle_products_without_mocks(self, mock_calculate_moments, mock_rotate_rtn_vectors_to_dps):
         epochs = np.array([datetime(2025, 3, 6)])
         mag_start_time = datetime(2025, 3, 6, 0, 1, 0)
         mag_epochs = np.array([mag_start_time + i * timedelta(seconds=1) for i in range(10)])
         swapi_epochs = np.array([datetime(2025, 3, 6), datetime(2025, 3, 10)])
-        swp_flags = np.array([SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED, SwapiL3Flags.SWP_SW_ANGLES_ESTIMATED])
+        swp_flags = np.zeros(2)
         mock_calculate_moments.return_value = build_swe_moment_data(len(epochs))
+        mock_rotate_rtn_vectors_to_dps.return_value = np.full((len(swapi_epochs), 3), np.nan)
         pitch_angle_bins = [70, 100, 130]
 
         num_energies = 9
@@ -634,9 +641,8 @@ def test_calculate_pitch_angle_products_without_mocks(self, mock_calculate_momen
         swapi_l3a_proton_data = SwapiL3aProtonData(
             epoch=swapi_epochs,
             epoch_delta=np.repeat(timedelta(seconds=30), 10),
-            proton_sw_speed=np.full(len(swapi_epochs), 400),
-            proton_sw_clock_angle=np.full(len(swapi_epochs), 0),
-            proton_sw_deflection_angle=np.full(len(swapi_epochs), 0),
+            proton_sw_velocity_rtn=np.full((len(swapi_epochs), 3), np.nan),
+            proton_sw_speed=np.full(len(swapi_epochs), 400.0),
             swp_flags=swp_flags
         )
         geometric_fractions = [0.0697327, 0.138312, 0.175125, 0.181759,
diff --git a/tests/test_data/swapi/reference_integrals.csv b/tests/test_data/swapi/reference_integrals.csv
new file mode 100644
index 000000000..f05e6e36b
--- /dev/null
+++ b/tests/test_data/swapi/reference_integrals.csv
@@ -0,0 +1,10001 @@
+bulk_speed,temperature_ev,bulk_azimuth,bulk_elevation,density,integral
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diff --git a/tests/test_utils.py b/tests/test_utils.py
index 4ce15e403..9c33322c5 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -48,18 +48,19 @@ def test_save_data(self, mock_science_file_path_class, mock_write_cdf, mock_toda
         input_metadata = InputMetadata("swapi", "l2", datetime(2024, 9, 17), datetime(2024, 9, 18), "v002",
                                        "descriptor", repointing=None)
         epoch = np.array([1, 2, 3])
-        alpha_sw_speed = np.array([4, 5, 6])
-        alpha_sw_density = np.array([5, 5, 5])
-        alpha_sw_temperature = np.array([4, 3, 5])
 
         data_product = SwapiL3AlphaSolarWindData(input_metadata=input_metadata, epoch=epoch,
-                                                 alpha_sw_speed=alpha_sw_speed,
-                                                 alpha_sw_temperature=alpha_sw_temperature,
-                                                 alpha_sw_density=alpha_sw_density,
+                                                 alpha_sw_density=np.array([5, 5, 5]),
+                                                 alpha_sw_density_uncert=np.array([0.1, 0.1, 0.1]),
+                                                 alpha_sw_temperature=np.array([4, 3, 5]),
+                                                 alpha_sw_temperature_uncert=np.array([0.1, 0.1, 0.1]),
+                                                 alpha_sw_velocity_rtn=np.zeros((3, 3)),
+                                                 alpha_sw_velocity_covariance_rtn=np.zeros((3, 3, 3)),
+                                                 alpha_sw_delta_v=np.array([0.0, 0.0, 0.0]),
+                                                 alpha_sw_delta_v_uncert=np.array([0.0, 0.0, 0.0]),
+                                                 alpha_sw_b_hat_rtn=np.zeros((3, 3)),
                                                  parent_file_names=sentinel.parent_files,
-                                                 bad_fit_flag=sentinel.bad_fit_flag,
-                                                 alpha_sw_pre_lut_density=sentinel.alpha_sw_pre_lut_density,
-                                                 alpha_sw_pre_lut_temperature=sentinel.alpha_sw_pre_lut_temperature,
+                                                 quality_flags=sentinel.quality_flags,
                                                  )
 
         mock_science_file_path = Mock()
@@ -196,19 +197,20 @@ def test_save_data_does_not_add_parent_attribute_if_empty(self, mock_write_cdf,
         input_metadata = InputMetadata("swapi", "l2", datetime(2024, 9, 17), datetime(2024, 9, 18), "v002",
                                        "descriptor")
         epoch = np.array([1, 2, 3])
-        alpha_sw_speed = np.array([4, 5, 6])
-        alpha_sw_density = np.array([5, 5, 5])
-        alpha_sw_temperature = np.array([4, 3, 5])
-        bad_fit_flag = np.repeat([SwapiL3Flags.NONE], 3)
+        quality_flags = np.repeat([SwapiL3Flags.NONE], 3)
 
         data_product = SwapiL3AlphaSolarWindData(input_metadata=input_metadata,
                                                  epoch=epoch,
-                                                 alpha_sw_speed=alpha_sw_speed,
-                                                 alpha_sw_temperature=alpha_sw_temperature,
-                                                 alpha_sw_density=alpha_sw_density,
-                                                 bad_fit_flag=bad_fit_flag,
-                                                 alpha_sw_pre_lut_density=sentinel.alpha_sw_pre_lut_density,
-                                                 alpha_sw_pre_lut_temperature=sentinel.alpha_sw_pre_lut_temperature,
+                                                 alpha_sw_density=np.array([5, 5, 5]),
+                                                 alpha_sw_density_uncert=np.array([0.1, 0.1, 0.1]),
+                                                 alpha_sw_temperature=np.array([4, 3, 5]),
+                                                 alpha_sw_temperature_uncert=np.array([0.1, 0.1, 0.1]),
+                                                 alpha_sw_velocity_rtn=np.zeros((3, 3)),
+                                                 alpha_sw_velocity_covariance_rtn=np.zeros((3, 3, 3)),
+                                                 alpha_sw_delta_v=np.array([0.0, 0.0, 0.0]),
+                                                 alpha_sw_delta_v_uncert=np.array([0.0, 0.0, 0.0]),
+                                                 alpha_sw_b_hat_rtn=np.zeros((3, 3)),
+                                                 quality_flags=quality_flags,
                                                  )
         save_data(data_product)
 
@@ -232,19 +234,20 @@ def test_save_data_custom_path(self, mock_write_cdf, mock_today, _):
         input_metadata = InputMetadata("swapi", "l2", datetime(2024, 9, 17), datetime(2024, 9, 18), "v002",
                                        "descriptor")
         epoch = np.array([1, 2, 3])
-        alpha_sw_speed = np.array([4, 5, 6])
-        alpha_sw_density = np.array([5, 5, 5])
-        alpha_sw_temperature = np.array([4, 3, 5])
-        bad_fit_flag = np.repeat([SwapiL3Flags.NONE], 3)
+        quality_flags = np.repeat([SwapiL3Flags.NONE], 3)
 
         data_product = SwapiL3AlphaSolarWindData(input_metadata=input_metadata,
                                                  epoch=epoch,
-                                                 alpha_sw_speed=alpha_sw_speed,
-                                                 alpha_sw_temperature=alpha_sw_temperature,
-                                                 alpha_sw_density=alpha_sw_density,
-                                                 bad_fit_flag=bad_fit_flag,
-                                                 alpha_sw_pre_lut_density=sentinel.alpha_sw_pre_lut_density,
-                                                 alpha_sw_pre_lut_temperature=sentinel.alpha_sw_pre_lut_temperature,
+                                                 alpha_sw_density=np.array([5, 5, 5]),
+                                                 alpha_sw_density_uncert=np.array([0.1, 0.1, 0.1]),
+                                                 alpha_sw_temperature=np.array([4, 3, 5]),
+                                                 alpha_sw_temperature_uncert=np.array([0.1, 0.1, 0.1]),
+                                                 alpha_sw_velocity_rtn=np.zeros((3, 3)),
+                                                 alpha_sw_velocity_covariance_rtn=np.zeros((3, 3, 3)),
+                                                 alpha_sw_delta_v=np.array([0.0, 0.0, 0.0]),
+                                                 alpha_sw_delta_v_uncert=np.array([0.0, 0.0, 0.0]),
+                                                 alpha_sw_b_hat_rtn=np.zeros((3, 3)),
+                                                 quality_flags=quality_flags,
                                                  )
 
         custom_path = TEMP_CDF_FOLDER_PATH / "fancy_path"
diff --git a/uv.lock b/uv.lock
index 4671b839a..7118eb515 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.12' and platform_machine == 'ARM64' and sys_platform == 'win32'",
@@ -714,6 +714,7 @@ dependencies = [
     { name = "lmfit" },
     { name = "netcdf4", version = "1.7.3", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'ARM64' and sys_platform == 'win32'" },
     { name = "netcdf4", version = "1.7.4", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'ARM64' or sys_platform != 'win32'" },
+    { name = "numba" },
     { name = "numdifftools" },
     { name = "numpy" },
     { name = "pandas" },
@@ -744,6 +745,7 @@ requires-dist = [
     { name = "imap-processing", specifier = ">=1.0.29" },
     { name = "lmfit", specifier = "==1.3.3" },
     { name = "netcdf4", specifier = ">=1.6" },
+    { name = "numba", specifier = ">=0.65.1" },
     { name = "numdifftools", specifier = "==0.9.41" },
     { name = "numpy", specifier = ">=2.0.1,<2.4" },
     { name = "pandas", specifier = "==2.2.3" },
@@ -911,6 +913,38 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/da/e9/0d4add7873a73e462aeb45c036a2dead2562b825aa46ba326727b3f31016/kiwisolver-1.4.9-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:fb940820c63a9590d31d88b815e7a3aa5915cad3ce735ab45f0c730b39547de1", size = 73929, upload-time = "2025-08-10T21:27:48.236Z" },
 ]
 
+[[package]]
+name = "llvmlite"
+version = "0.47.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/01/88/a8952b6d5c21e74cbf158515b779666f692846502623e9e3c39d8e8ba25f/llvmlite-0.47.0.tar.gz", hash = "sha256:62031ce968ec74e95092184d4b0e857e444f8fdff0b8f9213707699570c33ccc", size = 193614, upload-time = "2026-03-31T18:29:53.497Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f4/f5/a1bde3aa8c43524b0acaf3f72fb3d80a32dd29dbb42d7dc434f84584cdcc/llvmlite-0.47.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:41270b0b1310717f717cf6f2a9c68d3c43bd7905c33f003825aebc361d0d1b17", size = 37232772, upload-time = "2026-03-31T18:28:12.198Z" },
+    { url = "https://files.pythonhosted.org/packages/7c/fb/76d88fc05ee1f9c1a6efe39eb493c4a727e5d1690412469017cd23bcb776/llvmlite-0.47.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f9d118bc1dd7623e0e65ca9ac485ec6dd543c3b77bc9928ddc45ebd34e1e30a7", size = 56275179, upload-time = "2026-03-31T18:28:15.725Z" },
+    { url = "https://files.pythonhosted.org/packages/4d/08/29da7f36217abd56a0c389ef9a18bea47960826e691ced1a36c92c6ce93c/llvmlite-0.47.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9ea5cfb04a6ab5b18e46be72b41b015975ba5980c4ddb41f1975b83e19031063", size = 55128632, upload-time = "2026-03-31T18:28:19.946Z" },
+    { url = "https://files.pythonhosted.org/packages/df/f8/5e12e9ed447d65f04acf6fcf2d79cded2355640b5131a46cee4c99a5949d/llvmlite-0.47.0-cp310-cp310-win_amd64.whl", hash = "sha256:166b896a2262a2039d5fc52df5ee1659bd1ccd081183df7a2fba1b74702dd5ea", size = 38138402, upload-time = "2026-03-31T18:28:23.327Z" },
+    { url = "https://files.pythonhosted.org/packages/34/0b/b9d1911cfefa61399821dfb37f486d83e0f42630a8d12f7194270c417002/llvmlite-0.47.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:74090f0dcfd6f24ebbef3f21f11e38111c4d7e6919b54c4416e1e357c3446b07", size = 37232770, upload-time = "2026-03-31T18:28:26.765Z" },
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