{
  "abstract": "Previously, we found that adding an X-ray flux feature to a Random Forest classifier of active galactic nuclei (AGN) and star-forming galaxies (SFGs) coincided with a decrease in classification accuracy from 97.51% to 89.26%, a counterintuitive result given the prevailing theory that X-rays are a reliable AGN diagnostic. We investigate this discrepancy through both astrophysical and machine learning lenses. On the astrophysical side, we show that the X-ray luminosities of AGN and SFGs in the sample substantially overlap across the canonical $10^{42}$ erg/s threshold, reflecting the moderate luminosity characteristic of an optically-selected sample and the contribution of high-mass X-ray binaries (HMXBs) to SFG emission. On the machine learning side, we argue that BPT-derived training labels carry instance-dependent label noise concentrated near the BPT demarcation line, where X-ray data would be most useful as a discriminator. We show that objects misclassified in 5-fold cross-validation cluster near the Kewley demarcation curve, with a median distance of 0.123 dex versus 0.743 dex for correctly classified objects (Kolmogorov-Smirnov $D = 0.745,\\; p = 1.67\\times 10^{-9}$). A controlled cross-validation decomposition shows that the previously reported decrease reflects predominantly sample selection: on a fixed sample, adding the X-ray feature leaves class-resolved metrics unchanged, and its permutation importance is consistent with zero across resampled splits. The classifier therefore makes negligible use of a physically informative feature, consistent with training labels coupled to the optical features. We conclude that future classifiers operating on optically-selected samples should employ independent label sources or noise-robust training methods.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.13233",
  "author_total": 1,
  "authors": [
    {
      "name": "Jaymin Ding"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3fyny812019hx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.13233",
  "fulltext_url": null,
  "id": "3fyny812019hx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.13233",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.GA"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "openalex",
      "3103|Galaxies: Formation, Evolution, Phenomena"
    ]
  ],
  "openalex": "W7168456102",
  "pdf_url": "https://arxiv.org/pdf/2607.13233",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3fyny812019hx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:52Z",
      "native_id": "2607.13233",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.13233"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.GA",
      "subfield": "galaxies"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "When X-ray Features Fail to Identify Intrinsic Emitters: Label Noise and Luminosity Overlap in Machine Learning Classification of AGN and Star-forming Galaxies",
  "title_text": "When X-ray Features Fail to Identify Intrinsic Emitters: Label Noise and Luminosity Overlap in Machine Learning Classification of AGN and Star-forming Galaxies",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:41Z",
  "url": "https://research.draconitic.com/paper/3fyny812019hx",
  "venue": "arXiv",
  "versions": []
}