{
  "abstract": "The Tibet AS$γ$ experiment observes cosmic gamma rays from several teraelectron volts to the petaelectron volt range using an array of ground-based surface air-shower detectors (Tibet-III), which is composed of plastic scintillators. The arrival direction is conventionally reconstructed by fitting the shower front to detector hit-timing data, and the resulting angular resolution is approximately 0.5$^\\circ$ to 0.2$^\\circ$ in the energy range from 10 to 100 TeV. In this study, to further improve the directional reconstruction accuracy, we developed a new arrival-direction reconstruction method that integrates a convolutional neural network (CNN) with the conventional method. Evaluations using gamma-ray events generated by Monte Carlo simulations show that the angular resolution is improved by approximately 10\\,\\%--15\\,\\% compared with that achieved with the conventional method. In addition, this performance improvement shows little dependence on the zenith angle up to 40$^\\circ$. The proposed method provides a performance gain equivalent to increasing the Tibet-III array, which consists of approximately 600 detectors, by roughly 150 to 200 detectors.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.09736",
  "author_total": 11,
  "authors": [
    {
      "name": "S. Okukawa"
    },
    {
      "name": "M. Kobayashi"
    },
    {
      "name": "K. Hara"
    },
    {
      "name": "K. Hibino"
    },
    {
      "name": "Y. Katayose"
    },
    {
      "name": "K. Kawata"
    },
    {
      "name": "M. Ohnishi"
    },
    {
      "name": "T. K. Sako"
    },
    {
      "name": "T. Sako"
    },
    {
      "name": "A. Shiomi"
    },
    {
      "name": "M. Takita"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5yt6b8f6gpm23.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.09736",
  "fulltext_url": null,
  "id": "5yt6b8f6gpm23",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.09736",
  "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.IM"
    ],
    [
      "openalex",
      "3106|Astrophysics and Cosmic Phenomena"
    ]
  ],
  "openalex": "W7221005158",
  "pdf_url": "https://arxiv.org/pdf/2610.09736",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5yt6b8f6gpm23.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:37Z",
      "native_id": "2610.09736",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.09736"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "A Deep Learning Approach to Improving the Arrival-Direction Accuracy of Gamma-Ray Air-Shower Measurements with the Tibet-III Air-Shower Array",
  "title_text": "A Deep Learning Approach to Improving the Arrival-Direction Accuracy of Gamma-Ray Air-Shower Measurements with the Tibet-III Air-Shower Array",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:37Z",
  "url": "https://research.draconitic.com/paper/5yt6b8f6gpm23",
  "venue": "arXiv",
  "versions": []
}