{
  "abstract": "Forward-folded X/$γ$-ray spectral fitting increasingly benefits from Bayesian workflows to better capture counting statistics, background uncertainties, and complex parameter degeneracies, while fully reproducible end-to-end implementations are still not always straightforward to deploy in routine analyses. We present ELISA (Efficient Library for Spectral Analysis), an open-source Python toolkit that streamlines the full spectral analysis loop for X/$γ$-ray astronomy: data loading, model construction, inference, and model validation and comparison. ELISA couples a JAX-accelerated forward model with a unified fitting interface, supporting both rapid optimization and modern Bayesian sampling. Built-in diagnostics make convergence checks and predictive assessment a standard part of the workflow, motivating principled model refinement when needed. Using a spectral fitting example of the Insight-HXMT Crab observation, we find substantial improvements in sampling efficiency compared with XSPEC's default MCMC implementation. ELISA is available at https://github.com/wcxve/elisa and on PyPI at https://pypi.org/project/astro-elisa.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.14587",
  "author_total": 14,
  "authors": [
    {
      "name": "Wang-Chen Xue"
    },
    {
      "name": "Sheng-Lun Xie"
    },
    {
      "name": "Chao Zheng"
    },
    {
      "name": "Shao-Lin Xiong"
    },
    {
      "name": "Xiao-Bo Li"
    },
    {
      "name": "Yong Chen"
    },
    {
      "name": "Peng Zhang"
    },
    {
      "name": "Yan-Qiu Zhang"
    },
    {
      "name": "Jia-Cong Liu"
    },
    {
      "name": "Chen-Wei Wang"
    },
    {
      "name": "Wen-Jun Tan"
    },
    {
      "name": "Yue Wang"
    },
    {
      "name": "Jin-Peng Zhang"
    },
    {
      "name": "Zheng-Hang Yu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/482m5jgenxgkx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.14587",
  "fulltext_url": null,
  "id": "482m5jgenxgkx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.14587",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.HE"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "openalex",
      "3103|Astrophysical Phenomena and Observations"
    ]
  ],
  "openalex": "W7221771169",
  "pdf_url": "https://arxiv.org/pdf/2610.14587",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/482m5jgenxgkx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:06Z",
      "native_id": "2610.14587",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.14587"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.HE",
      "subfield": "high-energy-astrophysics"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "ELISA: A Bayesian X/$γ$-ray Spectral Analysis Toolkit",
  "title_text": "ELISA: A Bayesian X/γ-ray Spectral Analysis Toolkit",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:06Z",
  "url": "https://research.draconitic.com/paper/482m5jgenxgkx",
  "venue": "arXiv",
  "versions": []
}