ENH: adding SSA tutorial

deploy_to_fornax_manifest.in

@@ -14,6 +14,7 @@ tutorials/simulated-data/openuniverse2024_roman_demodata_20000808_instances.csv
 tutorials/simulated-data/openuniverse2024_roman_simulated_timedomainsurvey.md
 tutorials/simulated-data/openuniverse2024_roman_simulated_wideareasurvey.md
 tutorials/simulated-data/roman_hlss_number_density.md
+tutorials/spitzer/plot_Spitzer_IRS_spectra.md
 tutorials/spherex/spherex_cutouts.md
 tutorials/spherex/spherex_intro.md
 tutorials/spherex/spherex_psf.md

notebook_metadata.yml

@@ -82,3 +82,6 @@
 - title: SPE Catalogs
   file: tutorials/euclid/5_Euclid_intro_SPE_catalog.md
   description: Join the SPE and MER catalogs and query for galaxies with H-alpha line detections, then plot the SIR spectrum of a galaxy with a high SNR H-alpha line measurement.
+- title: Spitzer IRS Spectra
+  file: tutorials/spitzer/plot_Spitzer_IRS_spectra.md
+  description: Discover IRSA's SSA collections, query for spectra, retrieve and visualize Spitzer IRS spectra.

toc.yml

@@ -53,6 +53,9 @@ project:
     - title: COSMOS
       children:
         - file: tutorials/cosmos/sia_v1_cosmos.md
+    - title: Spitzer
+      children:
+        - file: tutorials/spitzer/plot_Spitzer_IRS_spectra.md
     - title: Simulated Data
       file: tutorials/simulated-data/simulated.md
       children:

tutorials/spitzer/plot_Spitzer_IRS_spectra.md

@@ -0,0 +1,226 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.18.1
+kernelspec:
+  name: python3
+  display_name: python3
+  language: python
+---
+
+# Plot Spitzer IRS Spectra
+
+## Learning Goals
+
+By the end of this tutorial, you will be able to :
+
+- Discover which spectroscopy catalogs are available through IRSA’s Simple Spectral Access (SSA) service
+- Use Irsa.query_ssa() to search for spectra at specific sky positions
+- Retrieve and visualize infrared spectra from the IRSA archive
+- Adapt the workflow shown here to your own science use cases
+
+## Introduction
+
+In this tutorial we use a published sample of debris disk host stars to demonstrate how to search for and retrieve infrared spectra using IRSA’s SSA service.
+The targets are drawn from the catalog of debris disks compiled by Mittal et al. (2015, ApJ, 798, 87), which identifies stars exhibiting infrared excesses indicative of circumstellar dust.
+Debris disks trace the remnants of planet formation and are valuable probes of disk evolution, dust composition, and dynamical interactions with planets.
+
+Infrared spectroscopy is particularly powerful for studying debris disks because many of the diagnostic features of dust grains—such as silicate emission bands—appear at mid‑infrared wavelengths.
+The Spitzer Infrared Spectrograph (IRS) provides sensitive, low‑ and moderate‑resolution spectra in this regime, enabling measurements of dust temperature, composition, and structure.
+Querying the IRSA archive for Spitzer IRS observations allows us to connect published debris‑disk samples with archival spectroscopy and explore these systems in greater physical detail.
+
+### Instructions
+
+Feel free to adapt this notebook to your own targets and science goals.
+The functions and overall structure shown here are intended to be reusable for searching IRSA for spectra of interest using SSA.
+
+### Input
+
+- A list of sky positions (RA, Dec)
+
+### Output
+
+- Tables describing available spectra
+Optional plots of flux versus wavelength
+
+## Imports
+
+```{code-cell} ipython3
+# Uncomment the next line to install dependencies if needed.
+# !pip install numpy matplotlib astropy "astroquery>=0.4.10"
+```
+
+```{code-cell} ipython3
+import numpy as np
+import matplotlib.pyplot as plt
+
+from astropy.coordinates import SkyCoord
+import astropy.units as u
+from astropy.table import Table
+
+from astroquery.vizier import Vizier
+from astroquery.ipac.irsa import Irsa
+
+import warnings
+# suppress warnings about cache
+warnings.filterwarnings(
+    "ignore",
+    message="XDG_CACHE_HOME is set",
+    category=UserWarning,
+)
+```
+
+## 1. Exploring SSA catalogs available at IRSA
+Before querying for spectra, it is often useful to see which spectral collections are available through SSA.
+IRSA hosts spectra from multiple missions and instruments, each grouped into collections.
+
+```{code-cell} ipython3
+Irsa.list_collections(servicetype='ssa')
+```
+
+Each entry corresponds to a distinct spectral data collection (for example, Spitzer IRS enhanced products).
+You can use these collection names with `query_ssa(collection=...)` to control which archive holdings are searched.
+Users interested in other instruments or wavelength ranges are encouraged to explore this list and substitute a different collection name below.
+
++++
+
+## 2. Define the targets
+
+We begin by loading a published catalog of debris disk host stars from VizieR.
+
+```{code-cell} ipython3
+vizier = Vizier()
+vizier.ROW_LIMIT = 150  # Override the default limit of 50
+# VizieR catalog identifier for Mittal et al. (2015)
+mittal = "J/ApJ/798/87"
+
+debris_disks = vizier.get_catalogs(mittal)
+```
+
+### 2.1 Explore the target catalog
+
+Let’s inspect the contents of the returned catalog and identify the columns we need.
+
+```{code-cell} ipython3
+debris_disks
+```
+
+```{code-cell} ipython3
+#we really want the first table, not the refs table
+debris_disks = debris_disks[0]
+debris_disks
+```
+
+```{code-cell} ipython3
+debris_disks.colnames
+```
+
+## 3. Find IRSA spectroscopy
+
+Lets see if any of these debris disks have spectra in the IRSA archive
+
+```{code-cell} ipython3
+# IRSA queries require sky coordinates,
+# so we convert the RA and Dec columns into a vectorized SkyCoord object.
+coords = SkyCoord(ra=debris_disks["_RA"],
+                  dec=debris_disks["_DE"],
+                  unit=u.deg,
+                  frame='icrs')
+
+# Just to make this tutorial run faster, we will limit the number of debris disks
+coords = coords[0:10]
+```
+
+The function below queries IRSA’s SSA service for Spitzer IRS enhanced spectra near each target position.
+It optionally plots the retrieved spectra and includes inline comments explaining each step.
+
+We provide this as a function so it can be easily lifted from this tutorial and used in your own work.
+
+```{code-cell} ipython3
+def query_and_plot_spectra(positions, *, plot=True, verbose=True):
+    """
+    Query IRSA for Spitzer IRS spectra near each target position using SSA.
+
+
+    Parameters
+    ----------
+    positions : astropy.coordinates.SkyCoord
+        Vectorized SkyCoord object containing target positions.
+    plot : bool, optional
+        If True, generate plots of flux versus wavelength.
+    verbose : bool, optional
+        If True, print status messages about query results.
+    """
+    # for each set of coordinates
+    for i, sc in enumerate(positions):
+        # Retrieve the target name from the debris disk catalog
+        target_name = debris_disks["Name"][i]
+
+        # Query the IRSA SSA service around this position
+        result = Irsa.query_ssa(pos=sc,
+                                radius=5*u.arcsec,
+                                collection='spitzer_irsenh')
+
+        # Handle cases with no available spectra
+        if result is None or len(result) == 0:
+            if verbose:
+                print(f"No IRS spectra available for target {target_name}")
+                continue
+
+        # Let the user know we have a winner
+        if verbose:
+            print(f"Found {len(result)} spectrum(s) for {target_name}")
+
+        # Loop through each spectrum returned for the object
+        for j, row in enumerate(result):
+
+            # Each SSA row includes an access URL pointing to the spectrum
+            spectrum_url = row['access_url']
+
+            # Read the spectrum into an Astropy Table
+            single_spec = Table.read(spectrum_url, format="ipac")
+
+            # If plotting is enabled, plot the spectrum.
+            if plot:
+                plt.figure()
+                if len(result) > 1:
+                    label = row['ObsID'] if 'ObsID' in row.colnames else f"Spectrum {j+1}"
+                    plt.plot(single_spec['wavelength'], single_spec['flux_density'], label=label)
+                else:
+                    plt.plot(single_spec['wavelength'], single_spec['flux_density'])
+
+                plt.xlabel("Wavelength (μm))")
+                plt.ylabel("Flux ")
+                plt.title(f"Spitzer IRS Spectrum for {target_name}")
+
+                # If more than one spectrum was found, add a legend
+                if len(result) > 1:
+                    plt.legend()
+
+                plt.tight_layout()
+                plt.show()
+```
+
+```{code-cell} ipython3
+query_and_plot_spectra(coords)
+```
+
+## Acknowledgements
+
+- [IPAC-IRSA](https://irsa.ipac.caltech.edu/)
+
+## About this notebook
+
+**Authors:** IPAC Science Platform Team, including Troy Raen, Brigitta Sipőcz, Jessica Krick, Andreas Faisst, Vandana Desai
+
+**Contact:** [IRSA Helpdesk](https://irsa.ipac.caltech.edu/docs/help_desk.html) with questions or problems.
+
+**Updated:** 2026-01-13
+
+**Runtime:** As of the date above, this notebook takes about 3 minutes to run to completion on a machine with 8GB RAM and 2 CPU.
+This runtime is heavily dependent on archive servers which means runtime will vary for users.
+
+**AI:** AI-based tools were used to assist in drafting and refining this tutorial, with all content reviewed and validated by the authors.