{
  "abstract": "We present a multi-method machine-learning study for the binary photometric classification of point-like and extended sources in the Rubin Observatory Data Preview 1 (DP1) and Data Preview 2 (DP2). The workflow runs on the Rubin Science Platform using DP1 and DP2 ugrizy photometry, the DP1 ancillary photometric-redshift catalog, and the Monster astrometric and photometric reference catalog, without relying on external spectroscopy. Six methods (SVM, Random Forest, XGBoost, k-Nearest Neighbors, an Artificial Neural Network, and a soft Voting ensemble) are benchmarked in the deepest DP1 field, the Extended Chandra Deep Field South. These methods reach 10-fold cross-validation macro-F1 scores of 0.95-0.97 on the balanced training subset and test macro-F1 scores of 0.87-0.92 on the imbalanced held-out set, with RF and the SV ensemble tied at the top (0.918); the differences among the leading methods are within the statistical uncertainty. Permutation importance identifies FWHM as the dominant feature, and the six methods agree on 79.5% of the full catalog. The same pipeline is then applied without retuning the settings to the two other DP1 fields with full ugrizy coverage and photometric-redshift support, Euclid Deep Field South and the Low Galactic Latitude Field. Together, the three fields span a factor of approximately 7 in E(B-V) and approximately 10 in stellar density, yet headline macro-F1 remains above 0.87 in every field. We then apply the same pipeline to four DP2 fields (ECDFS, COSMOS, EDFS, and ELAIS-S1). Using colors and morphology, the best method reaches a test macro-F1 of 0.83 to 0.88 across these fields, and the six methods agree on 77 to 86% of each catalog. Adding the per-band photometric uncertainties as features raises the best macro-F1 to about 0.95 in ECDFS DP1 and 0.97 in ECDFS DP2 (and to approximately 0.97-0.98 in the other fields).",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.20746",
  "author_total": 11,
  "authors": [
    {
      "name": "Andrés Alejandro Plazas Malagón"
    },
    {
      "name": "Yasna Ordenes-Briceño"
    },
    {
      "name": "Dan S. Taranu"
    },
    {
      "name": "Alex Broughton"
    },
    {
      "name": "Yumi Choi"
    },
    {
      "name": "Jeffrey L. Carlin"
    },
    {
      "name": "Sophie Reed"
    },
    {
      "name": "Karla Peña Ramírez"
    },
    {
      "name": "Sean Patrick MacBride"
    },
    {
      "name": "Hurum Maksora Tohfa"
    },
    {
      "name": "Arianna Ranabhat"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8mq59t5fw7fc2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.20746",
  "fulltext_url": null,
  "id": "8mq59t5fw7fc2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.20746",
  "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"
    ],
    [
      "openalex",
      "3103|Galaxies: Formation, Evolution, Phenomena"
    ]
  ],
  "openalex": "W7222571730",
  "pdf_url": "https://arxiv.org/pdf/2610.20746",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8mq59t5fw7fc2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:50Z",
      "native_id": "2610.20746",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.20746"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Multi-Method Machine Learning Classification of Astronomical Sources with Rubin Data Previews: a Spectroscopy-Free Approach with DP1 and DP2",
  "title_text": "Multi-Method Machine Learning Classification of Astronomical Sources with Rubin Data Previews: a Spectroscopy-Free Approach with DP1 and DP2",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:50Z",
  "url": "https://research.draconitic.com/paper/8mq59t5fw7fc2",
  "venue": "arXiv",
  "versions": []
}