{
  "abstract": "Astronomical spectra encode rich physical information, but drawing scientific conclusions from spectral features typically requires expert interpretation. This paper presents AstroSpecLM, a spectrum-language model that connects one-dimensional DESI spectra with Qwen3-4B to answer questions and provide explanations grounded in spectral evidence. Instead of generating question-answer pairs directly from templates or raw catalog fields, we first distill each spectrum into a compact set of catalog- and spectrum-derived facts, then use these facts as references to generate instruction-following conversations. The resulting model is competitive with specialist supervised baselines on classification and redshift estimation, while additionally producing natural-language explanations that reference specific spectral features. Our results indicate that grounding a language model in one-dimensional scientific spectra is feasible, and that fact-mediated instruction data yields a model capable of both prediction and explanation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.07102",
  "author_total": 5,
  "authors": [
    {
      "name": "Jinghang Shi"
    },
    {
      "name": "Yanxia Zhang"
    },
    {
      "name": "Ali Luo"
    },
    {
      "name": "Changhua Li"
    },
    {
      "name": "Xiao Kong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fwdptwj1fk828.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.07102",
  "fulltext_url": null,
  "id": "fwdptwj1fk828",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.07102",
  "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.IM"
    ],
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "openalex",
      "3103|Galaxies: Formation, Evolution, Phenomena"
    ]
  ],
  "openalex": "W7212078323",
  "pdf_url": "https://arxiv.org/pdf/2609.07102",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fwdptwj1fk828.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:48Z",
      "native_id": "2609.07102",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.07102"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "AstroSpecLM: A Spectrum-Language Model for Evidence-Grounded Astronomical Spectral Analysis",
  "title_text": "AstroSpecLM: A Spectrum-Language Model for Evidence-Grounded Astronomical Spectral Analysis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:48Z",
  "url": "https://research.draconitic.com/paper/fwdptwj1fk828",
  "venue": "arXiv",
  "versions": []
}