{
  "abstract": "We present a machine-learning pipeline for determining stellar parameters and chemical abundances from spectra observed by the Dark Energy Spectroscopic Instrument (DESI). Building on the model-driven Payne framework, we train an artificial neural network on ~370,000 synthetic spectra spanning FGK stars, and fit the network directly to the flux-calibrated, un-normalised spectral energy distribution (SED) of each star, rather than to a continuum-normalised spectrum as done by DESI's official Stellar Parameter (SP) pipeline. Internal accuracy tests show a median interpolation error of less than 0.3% for 90% of a synthetic verification sample, and the full fitting workflow recovers input labels to high accuracy even at low signal-to-noise ratio (S/N). Validating our method on cross-matched samples of 6719 APOGEE and 3455 GALAH stars observed by DESI with S/N > 20, we recover Teff and logg with smaller systematic offsets than the DESI SP pipeline, an improvement we attribute to fitting the full SED shape rather than a normalised spectrum. We further recover 12 elemental abundances (Na, Mg, Al, Si, Ca, Ti, V, Cr, Mn, Ni, Ba, and Y) to an accuracy that matches or exceeds SP where a comparison is possible, and provide four abundances, Mn, V, Ba, and Y, that SP does not report at all. Applying this pipeline to the DESI Data Release 1 stellar sample, we deliver a catalogue of stellar parameters and abundances for 3.2 million stars.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.04294",
  "author_total": 3,
  "authors": [
    {
      "name": "Nagaraj Vernekar"
    },
    {
      "name": "Michael Hayden"
    },
    {
      "name": "Sara Lucatello"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bf4xce1bv8r8r.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.04294",
  "fulltext_url": null,
  "id": "bf4xce1bv8r8r",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.04294",
  "language": null,
  "licence": "CC0-1.0",
  "licence_label": "CC0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/publicdomain/zero/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.GA"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "arxiv",
      "astro-ph.SR"
    ],
    [
      "openalex",
      "3103|Stellar, planetary, and galactic studies"
    ]
  ],
  "openalex": "W7220724583",
  "pdf_url": "https://arxiv.org/pdf/2610.04294",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bf4xce1bv8r8r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:01Z",
      "native_id": "2610.04294",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.04294"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.GA",
      "subfield": "galaxies"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.SR",
      "subfield": "stellar"
    }
  ],
  "title": "Stellar parameters and abundances for 3.2 million DESI DR1 spectra using machine learning",
  "title_text": "Stellar parameters and abundances for 3.2 million DESI DR1 spectra using machine learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:01Z",
  "url": "https://research.draconitic.com/paper/bf4xce1bv8r8r",
  "venue": "arXiv",
  "versions": []
}