{
  "abstract": "We present jax-SNANA, a JAX reimplementation of the supernova analysis framework SNANA. jax-SNANA enables GPU-accelerated transient simulations and light curve fitting, supernova standardisation and cosmological inference. Running in single precision on an NVIDIA A100 GPU, jax-SNANA simulates more than 100,000 LSST-observed supernovae per second and fits 5,000 light curves per second after JIT compilation, a two orders-of-magnitude increase over current tools, essential for analysing samples at the scale expected from LSST. jax-SNANA currently supports the SALT and BayeSN spectral energy distribution (SED) models as well as physical models from redback-jax and a variety of SN SED templates; light curve fitting is supported with both photometric and spectroscopic redshifts. We fully validate the accuracy of jax-SNANA by comparing to SNANA, finding excellent consistency between the two across a variety of tasks. The code provides a modular JAX end-to-end supernova cosmology pipeline, user-extensible to support new features including different SED templates and photometric classification algorithms. jax-SNANA has been designed with future applications of simulation-based inference in mind, providing fast simulations and enabling fully compiled on-the-fly simulations to be generated within the training loop of downstream SBI tasks. jax-SNANA extends beyond SNANA by supporting simulations of generic SN-host galaxy relationships and multiple sub-populations of events, for example originating from young and old progenitors. jax-SNANA is publicly available at https://github.com/mattgrayling/jax-snana and installable through pip.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.19000",
  "author_total": 1,
  "authors": [
    {
      "name": "Matthew Grayling"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b5nzc54h8mbx5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.19000",
  "fulltext_url": null,
  "id": "b5nzc54h8mbx5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.19000",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.CO"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "openalex",
      "3103|Gamma-ray bursts and supernovae"
    ]
  ],
  "openalex": "W7221982499",
  "pdf_url": "https://arxiv.org/pdf/2610.19000",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/b5nzc54h8mbx5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:31Z",
      "native_id": "2610.19000",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.19000"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.CO",
      "subfield": "cosmology"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "jax-SNANA: A GPU-accelerated Supernova Framework for the SBI Era",
  "title_text": "jax-SNANA: A GPU-accelerated Supernova Framework for the SBI Era",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:31Z",
  "url": "https://research.draconitic.com/paper/b5nzc54h8mbx5",
  "venue": "arXiv",
  "versions": []
}