{
  "abstract": "This white paper summarizes scientific challenges and AI/ML research opportunities identified through the FAIRS Japan 2024 unconference process. The discussion focuses on three major physics domains: accelerator physics, cosmology and astrophysics, and neutrino physics. Although each domain has distinct scientific goals and experimental constraints, several common technical themes emerge: high-dimensional reconstruction, fast and accurate simulation, uncertainty propagation, simulation-to-data mismatch, anomaly detection, real-time decision-making, and shared infrastructure.",
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  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.27807",
  "author_total": 19,
  "authors": [
    {
      "name": "Yoshitaka Itow"
    },
    {
      "name": "Jia Liu"
    },
    {
      "name": "Hirokazu Maesaka"
    },
    {
      "name": "Vinicius Mikuni"
    },
    {
      "name": "Nhat-Minh Nguyen"
    },
    {
      "name": "Hironao Miyatake"
    },
    {
      "name": "Atsushi J. Nishizawa"
    },
    {
      "name": "Patrick de Perio"
    },
    {
      "name": "Daniel Ratner"
    },
    {
      "name": "Kazuhiro Terao"
    },
    {
      "name": "Leander Thiele"
    },
    {
      "name": "Omar Alterkait"
    },
    {
      "name": "Francois Drielsma"
    },
    {
      "name": "Rocio Garcia"
    },
    {
      "name": "Masako Iwasaki"
    },
    {
      "name": "Ahsani Hafizhu Shali"
    },
    {
      "name": "Federica Tarsitano"
    },
    {
      "name": "Takahiro Terada"
    },
    {
      "name": "Junjie Xia"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/081zzpy34vf6q.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.27807",
  "fulltext_url": null,
  "id": "081zzpy34vf6q",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.27807",
  "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.IM"
    ],
    [
      "arxiv",
      "hep-ex"
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    [
      "arxiv",
      "hep-ph"
    ],
    [
      "arxiv",
      "physics.acc-ph"
    ],
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      "1702|Artificial Intelligence Applications"
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  ],
  "openalex": "W7204780182",
  "pdf_url": "https://arxiv.org/pdf/2608.27807",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/081zzpy34vf6q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:03Z",
      "native_id": "2608.27807",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.27807"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.acc-ph",
      "subfield": "applied-instrumentation"
    },
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Future of Artificial Intelligence for Science in Japan 2024 Community Report",
  "title_text": "Future of Artificial Intelligence for Science in Japan 2024 Community Report",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:03Z",
  "url": "https://research.draconitic.com/paper/081zzpy34vf6q",
  "venue": "arXiv",
  "versions": []
}