MCSNRcat

A living, multiwavelength census of supernova remnants in the Magellanic Clouds — the MC counterpart to Green's Galactic SNR catalogue and SNRcat. Currently compiles 80 confirmed + 44 candidate SNRs in LMC from literature. SMC SNRs to be cataloged later.

Live site: https://whyvav.github.io/MCSNRcat/

Each object page includes a multiwavelength viewer (DSS2 optical, SHASSA Hα, eROSITA-DE DR1 X-ray, XMM-Newton EPIC, RACS-low & SUMSS radio, GALEX UV, AllWISE mid-IR, 2MASS near-IR via Aladin Lite), X-ray/radio/energetics properties, and one-click links out to SIMBAD, ESASky, ADS, and VizieR. Locally, object pages also show pipeline-generated cutout PNGs (eROSITA / DeMCELS Hα & [S II] / ASKAP 888 MHz — see images/README.md); this project is unpublished/private for now, so those images stay gitignored and off the public repo/site (see "Imagery" below).

Classification criteria

An object is a confirmed SNR when it satisfies at least two of the three classical criteria (Filipović et al. 1998; Bozzetto et al. 2017): (1) non-thermal radio spectral index α < −0.4; (2) diffuse X-ray emission; (3) shock-enhanced [S II]/Hα ≥ 0.4. One criterion → candidate.

Sources

• Maggi et al. 2016, A&A 585, A162 (XMM-Newton X-ray population)
• Bozzetto et al. 2017, ApJS 230, 2 (radio/statistical)
• Leahy 2017, ApJ 837, 36 (energetics)
• Yew et al. 2021, MNRAS 500, 2336 (optical)
• Maitra et al. 2019, MNRAS 490, 5494 (J0513-6724)
• Maitra et al. 2021, MNRAS 504, 326 (J0507-6847)
• Sasaki et al. 2022, A&A 661, A37 (eROSITA; J0529-7004)
• Filipović et al. 2022, MNRAS 512, 265 (J0624-6948 discovery)
• Kavanagh et al. 2022, MNRAS 515, 4099 (XMM faint/evolved)
• Bozzetto et al. 2022, MNRAS 518, 2574 (ASKAP)
• Zangrandi et al. 2024, A&A 692, A237 (eROSITA census)
• Zangrandi et al. 2026, A&A 709, A207 (eROSITA Paper II: spectra + XLF)
• Sasaki et al. 2025, A&A 693, L15 (XMM/MeerKAT; J0614-7251 & J0624-6948)
• Ramos-Ceja et al. 2026, A&A 712, A171 (eROSITA-DE DR2 / eRASS:3 source catalogue)
• Tubín-Arenas et al. 2024, A&A 682, A35 (eROSITA upper-limit server)
• Shukla 2024, MSc Thesis (consolidation; J0500-6512 confirmation)

Architecture

Versioned CSV → build.py → static site. No database, no server code, no Excel. The CSV data/lmc_snrs_extended_v<N>.csv is the hand-editable source of truth; build.py auto-selects the highest version and validates it. catalog.py provides safe helper commands (confirm, set, validate, new-version) for routine updates. VLMism reads this CSV for its Phase-1 catalog (it no longer produces it).

v6 adds an eROSITA-DE DR2 (eRASS:3) cross-match: an erass3_* block giving every object either a detection (name, separation, detection/extent likelihood, 0.2–2.3 keV flux) or a 3σ flux upper limit, plus the Poisson chance of a coincidental match at that aperture. It is produced by erosita_dr2.py (fetch → data/erosita_dr2_erass3.csv, then merge into a new catalog version) and changes no object's classification — see "eROSITA eRASS:3 cross-match" below.

v4 is a systematic re-extraction of every object's record across all cited papers, adding new derived properties (cross-checked SN types, literature ages/energetics, eROSITA spectral fits and X-ray luminosity-function parameters) without changing the v3 confirmed/candidate census or IDs.

MCSNRcat/
├── data/lmc_snrs_extended_v<N>.csv  ← input catalog (source of truth; latest wins)
├── data/erosita_dr2_erass3.csv      ← eRASS:3 cross-match product (124 rows, with provenance)
├── build.py                         ← generator + validator (pandas + stdlib)
├── catalog.py                       ← maintenance CLI (confirm / set / validate / new-version)
├── erosita_dr2.py                   ← eROSITA-DE DR2 cross-match CLI (fetch / merge)
├── tests/                           ← pytest suite (offline; run with `python -m pytest`)
├── images/                          ← multiwavelength cutout PNGs + manifest.csv
│   └── <slug>/<slug>_<band>.png     generated by VLMism scripts/04_build_snr_images.py;
│                                     gitignored (local-only, see "Imagery" below)
├── site/                            ← built output (gitignored; CI rebuilds it)
│   ├── index.html                   census table + filters + clickable sky map
│   ├── objects/<ID>.html            ×124: Aladin Lite viewer, cutout panel, grouped
│   │                                properties, SIMBAD/ESASky/ADS/VizieR links
│   ├── about.html                   criteria, sources, citation, imagery credits
│   └── catalog.{csv,json}           machine-readable downloads
└── .github/workflows/deploy.yml     GitHub Pages CI

Static by design: free, permanent, versionable, zero maintenance, trivially mirrored/archived. Streamed survey imagery comes client-side from HiPS (Aladin Lite). build.py can additionally pick up curated per-object cutout PNGs under images/ (provenance in images/manifest.csv), produced by the VLMism Phase-2 pipeline from eROSITA-DE DR1, DeMCELS DR1, and ASKAP-EMU data — but locally only for now.

eROSITA eRASS:3 cross-match

Every object carries an eROSITA eRASS:3 detection or a 3σ flux upper limit (Ramos-Ceja et al. 2026; limits from Tubín-Arenas et al. 2024), in the erass3_* columns. This is a completeness, uniformity and independence improvement, not a new result: DR2 adds no remnants and supersedes none of the published eROSITA analyses. What it adds is one measurement per object from a single pipeline — where the rest of the X-ray block is three papers with three selection functions — produced by a team independent of this catalog's sources.

Two things the columns must not be read as:

• A detection is not a confirmation. An eRASS:3 entry may be any X-ray source inside the aperture, while the classification criteria concern diffuse X-ray emission from the remnant. No erass3_* column feeds klass; erosita_dr2.py merge refuses to write if the identity or classification columns move, and tests/test_erosita_dr2.py tests it.
• At eRASS:3 depth a detection is nearly free. The LMC field carries ~250 sources deg⁻², so erass3_p_chance (the Poisson chance of a coincidental match at that object's aperture) is reported next to every detection, and erass3_flag_lga marks detections the release flags as possible blends inside the galaxy's own overdensity.

Reproduce it (network; responses cached under data/raw/, gitignored):

python erosita_dr2.py fetch          # eRODat cone search + upper limits -> data/erosita_dr2_erass3.csv
python catalog.py new-version        # never edit a published version
python erosita_dr2.py merge          # write the erass3_* columns into the new version

Only pandas + numpy + the standard library are needed — the archive is queried over urllib, and DR2 is a static release, so a re-run reproduces these numbers.

Imagery

images/ (pipeline cutout PNGs + manifest.csv, written by VLMism scripts/04_build_snr_images.py) is gitignored — see images/README.md for its layout. When present, build.py copies it into site/images/ and each object page's "Multiwavelength cutouts" panel (plus the About page's imagery credits and images/manifest.csv links) appears automatically; when absent — as in the GitHub Actions checkout that builds the public site — build.py skips it silently and those sections are simply omitted, so the public site never links to cutouts it doesn't have.

Run locally

python build.py --out site          # auto-selects the latest catalog version
python -m http.server -d site 8000     # open http://localhost:8000
python -m pytest -q                 # offline test suite (pandas, numpy, pytest)

(Opening site/index.html directly via a file:// URL will render the census table but not the sky viewers — browsers block ES module imports and some fetches on file:// origins. Always preview through a local server.)

Data & feedback

Download: CSV · JSON. Corrections, new objects, or missing references: please open an issue.

How to cite

Until the accompanying paper is published, please cite this website by URL & data version, and the Master's Thesis this catalog builds on:

@mastersthesis{Shukla2024_MThesis,
  title = {{X}-ray {Evolution} of {Supernova} {Remnants} in the {Large} {Magellanic} {Cloud}},
  shorttitle = {{X}-ray {Evolution} of {LMC} {SNRs}},
  url = {https://www.sternwarte.uni-erlangen.de/docs/theses/2024-11_Shukla.pdf},
  language = {en},
  school = {FAU Erlangen-N{\"u}rnberg},
  author = {Shukla, Vaibhav},
  month = nov,
  year = {2024}
}

License

Data and content are released under CC-BY 4.0 — reuse is welcome with attribution. See LICENSE.

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Maintained by V. Shukla.