{
  "abstract": "Space-based photometry from Kepler and TESS has delivered oscillation spectra for hundreds of thousands of red giants, and PLATO and the Roman Galactic Bulge Time-Domain Survey will add more. We present ROSE, a modular machine-learning framework for automated asteroseismic characterisation of red giants, and apply two of its modules to the frequency of maximum oscillation power, $ν_{\\rm max}$, and the large frequency separation, $Δν$. Each module is a neural network bundled with its own preprocessing transform and output binning, so that each parameter is measured in the representation that displays it most effectively: $ν_{\\rm max}$ from the power-density spectrum, and $Δν$ from the power spectrum of the power spectrum taken over a window centred on $ν_{\\rm max}$. Both are trained only on synthetic spectra generated at a one-year baseline and both return probability distributions rather than point estimates. This enables us to retrieve asymmetric uncertainty and a reliability index. Validating against held-out synthetic spectra, Kepler spectra at the training resolution and TESS continuous-viewing-zone giants, we obtain robust dispersions of 1.4, 3.3 and 4.4% in $ν_{\\rm max}$ and 0.35, 0.61 and 1.3% in $Δν$, with the $ν_{\\rm max}$ uncertainties close to their nominal coverage. The fully automated pipeline was applied to 274,721 TESS red-giant candidates observed for at least ten sectors, the framework returning reliable $ν_{\\rm max}$ for 56,224 stars and reliable $Δν$ for 39,939, taking 8 ms per star. The sample follows $Δν= 0.278 ν_{\\rm max}^{0.757}$ with a scatter of 6.2%, and the red-clump phase stands out as a distinct overdensity, although neither module is provided an evolutionary label or asked to produce one.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.10312",
  "author_total": 4,
  "authors": [
    {
      "name": "Nipun Ghanghas"
    },
    {
      "name": "Dinil B. Palakkatharappil"
    },
    {
      "name": "Rafael A. Garcia"
    },
    {
      "name": "Shravan Hanasoge"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dvv0b58zphzks.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.10312",
  "fulltext_url": null,
  "id": "dvv0b58zphzks",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.10312",
  "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",
      "astro-ph.SR"
    ],
    [
      "openalex",
      "3103|Stellar, planetary, and galactic studies"
    ]
  ],
  "openalex": "W7220961330",
  "pdf_url": "https://arxiv.org/pdf/2610.10312",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dvv0b58zphzks.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:37Z",
      "native_id": "2610.10312",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.10312"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.SR",
      "subfield": "stellar"
    }
  ],
  "title": "ROSE (Red-giant Oscillations Spectra Estimator): A Modular Machine-Learning Framework for Automated Asteroseismic Characterisation. I. $ν_{\\max}$ and $Δν$ from TESS",
  "title_text": "ROSE (Red-giant Oscillations Spectra Estimator): A Modular Machine-Learning Framework for Automated Asteroseismic Characterisation. I. ν_max and Δν from TESS",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:37Z",
  "url": "https://research.draconitic.com/paper/dvv0b58zphzks",
  "venue": "arXiv",
  "versions": []
}