{
  "abstract": "Aurora visibility at a given location requires two physically distinct conditions to hold at once: aurora occurring overhead, governed by solar wind-magnetosphere coupling, and observing conditions that permit detection, governed by cloud cover and moonlight. Approaches that conflate the two weaken the space-weather signal and limit cross-site generalizability. We develop Aurora Hunter, a two-stage cascade that separates occurrence prediction from observing-condition assessment. Stage 1 uses 43 physics-driven features to predict P(aurora identifiable in all-sky images) via gradient-boosted trees trained on joint Tromso+Kiruna data (about 16,600 hours, 2015-2023). Stage 2 models P(unobscured image class | identifiable) with logistic regression on 15 observing-condition features, trained on hours with identifiable aurora. The cascade P(visible) = P(identifiable) x P(unobscured | identifiable) achieves retrospective ROC-AUC of 0.958 (Tromso test, 2019-2020) and 0.933 (independent Kiruna, 2024), improving on the occurrence stage used alone by +0.095 and +0.097. Transfer to Skibotn, the one station withheld entirely (2022-2025), is limited. SHAP analysis identifies magnetic local time, the Kp x nightside interaction and the three-hour mean Kp as the dominant features (44% of attribution), consistent with auroral oval physics. Hemisphere-wide occurrence maps combine the Stage 1 amplitude, on a clear-sky basis, with the Feldstein oval parameterization. By providing location-specific visibility probabilities with measured reliability rather than coarse geomagnetic indices, the framework links space weather research to practical observation planning. An operational proof of concept is available at https://aurora-hunter.onrender.com",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.24038",
  "author_total": 7,
  "authors": [
    {
      "name": "Zongyuan Ge"
    },
    {
      "name": "Chenwaner Zhang"
    },
    {
      "name": "Haoyang Li"
    },
    {
      "name": "Hantai Zhang"
    },
    {
      "name": "Wei Zhou"
    },
    {
      "name": "Wenxin Gu"
    },
    {
      "name": "Zhaoming Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/457cbmkk465my.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.24038",
  "fulltext_url": null,
  "id": "457cbmkk465my",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.24038",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "astro-ph.EP"
    ],
    [
      "arxiv",
      "astro-ph.IM"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.space-ph"
    ],
    [
      "openalex",
      "3103|Ionosphere and magnetosphere dynamics"
    ]
  ],
  "openalex": "W7162436925",
  "pdf_url": "https://arxiv.org/pdf/2605.24038",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/457cbmkk465my.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:19Z",
      "native_id": "2605.24038",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.24038"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.EP",
      "subfield": "planetary"
    },
    {
      "field": "astronomy",
      "origin": "arxiv:physics.space-ph",
      "subfield": "stellar"
    }
  ],
  "title": "Aurora Hunter: A Two-Stage Framework for Probabilistic Visibility Forecasting",
  "title_text": "Aurora Hunter: A Two-Stage Framework for Probabilistic Visibility Forecasting",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:19Z",
  "url": "https://research.draconitic.com/paper/457cbmkk465my",
  "venue": "arXiv",
  "versions": []
}