{
  "abstract": "Very-long-baseline interferometry (VLBI) may enable direct imaging of fine, event-horizon scale black hole structures such as the photon ring. In particular, the $n=1$ photon subring encodes information about black hole spin in its radial profile and azimuthal brightness modulation, making it a key proxy for spacetime properties. However, the $n=1$ subring overlaps with emission from the $n=0$ subring in observations, requiring isolation of the $n=1$ subring for measurements. We present a convolutional neural network (CNN) capable of extracting the $n=1$ subring from composite images containing $n=0$ and $n=1$ subrings. We simulate $111,000$ $m$-ring images with \\texttt{eht-imaging} to train and validate the CNN. The CNN accurately recovers the radial and angular brightness profiles of the $n=1$ subring with a mean normalized cross correlation (NXcorr) of $\\sim0.99$. The CNN provides a detection capability by predicting no false positives when input images lack a $n=1$ subring. We perform feature extraction with \\texttt{ringfit} to show that the CNN-predicted $n=1$ subring accurately recovers the ground truth radial profiles and azimuthal brightness modulation, demonstrating the CNN's capability to extract spin-sensitive features. We further test the CNN on black hole images generated by \\texttt{KerrBAM} and general relativistic magnetohydrodynamic (GRMHD) simulations, finding that it recovers the overall $n=1$ subring radial profiles while exhibiting discrepancies in the recovered intensity profiles. These results demonstrate the potential of deep learning methods to isolate the $n=1$ subring from overlapping emission, providing a framework for analyzing future high resolution black hole images from proposed VLBI missions such as the Black Hole Explorer (BHEX).",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.25344",
  "author_total": 3,
  "authors": [
    {
      "name": "Courtney Duong"
    },
    {
      "name": "Frank Myhre"
    },
    {
      "name": "Joseph R. Farah"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6dw02h4hw845j.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.25344",
  "fulltext_url": null,
  "id": "6dw02h4hw845j",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.25344",
  "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.HE"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "openalex",
      "3103|Astrophysical Phenomena and Observations"
    ]
  ],
  "openalex": "W7214121149",
  "pdf_url": "https://arxiv.org/pdf/2609.25344",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6dw02h4hw845j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:39Z",
      "native_id": "2609.25344",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.25344"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.HE",
      "subfield": "high-energy-astrophysics"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Convolutional Neural Network for Extraction of $n = 1$ Photon Ring of Black Holes",
  "title_text": "Convolutional Neural Network for Extraction of n = 1 Photon Ring of Black Holes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:39Z",
  "url": "https://research.draconitic.com/paper/6dw02h4hw845j",
  "venue": "arXiv",
  "versions": []
}