{
  "abstract": "Sagittarius A* exhibits frequent flaring activity across the electromagnetic spectrum that is often associated with a localized region of strong emission known as a hot spot. We aim to train a deep learning model to provide a link between key parameters of this phenomenon - hot-spot emission radius, and black hole inclination and spin - to the observed angle difference between the primary and secondary image ($ΔPA$) that present and future interferometric arrays could resolve. Using the general relativistic radiative transfer code IPOLE, we generated a library of $\\sim100.000$ models with varying system parameters and computed the position angle difference on the image plane between the primary and secondary images of the hot spot. We explore equatorial and non-equatorial circular orbits and evaluate our models against approximate observational constraints, including partial-orbit visibility and observational errors. Our algorithm STIHOS shows remarkable accuracy in calculating spin and inclination from the majority of the observational tests we perform ($σ_{a_*}=0.04,\\,σ_i=2^{\\circ}$), even in extreme conditions where only half of the orbit is visible. The off-equatorial estimation provides softer constraints in the absence of prior information. Our results demonstrate the importance of hot-spot observations for spacetime estimations. Given the increasing efforts to detect the photon-ring, our framework could prove valuable in interpreting the first observations of lensed emission.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.18208",
  "author_total": 2,
  "authors": [
    {
      "name": "Aristomenis I. Yfantis"
    },
    {
      "name": "Rami Al-Belmpeisi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1d9c5c23bx8xq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.18208",
  "fulltext_url": null,
  "id": "1d9c5c23bx8xq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.18208",
  "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": null,
  "pdf_url": "https://arxiv.org/pdf/2608.18208",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1d9c5c23bx8xq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:52Z",
      "native_id": "2608.18208",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.18208"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.HE",
      "subfield": "high-energy-astrophysics"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "A deep learning algorithm for black hole spin estimation using hot-spot secondary images",
  "title_text": "A deep learning algorithm for black hole spin estimation using hot-spot secondary images",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:41Z",
  "url": "https://research.draconitic.com/paper/1d9c5c23bx8xq",
  "venue": "arXiv",
  "versions": []
}