{
  "abstract": "Modern machine learning techniques have become increasingly important in particle physics because of their powerful pattern-recognition capabilities, including in real-time data acquisition where stringent runtime constraints apply. This paper details the performance of deep-learning-based trigger algorithms for a large water Cherenkov detector such as Hyper-Kamiokande, aimed at low-energy neutrino events (below 7 MeV). The performance of custom neural-network supervised classifiers is shown alongside two anomaly-detection approaches trained solely on detector noise: a pure autoencoder and a model based on Manifold Projection-Diffusion Recovery. The supervised model shows signal identification efficiencies of 76.7% for single electrons of 3 MeV kinetic energy, significantly exceeding signal efficiencies obtained from a traditional hit-count-based trigger of 26.4%, while the Manifold Projection-Diffusion Recovery approach reaches 35.4% at the same operating point. Runtime evaluations on GPU yield per-window inference latencies well below the millisecond scale.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.31391",
  "author_total": 4,
  "authors": [
    {
      "name": "Katharina Lachner"
    },
    {
      "name": "Saúl Alonso-Monsalve"
    },
    {
      "name": "Benjamin Richards"
    },
    {
      "name": "Davide Sgalaberna"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/80j13kzx6z1my.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.31391",
  "fulltext_url": null,
  "id": "80j13kzx6z1my",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.31391",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "hep-ex"
    ],
    [
      "arxiv",
      "physics.ins-det"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2605.31391",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/80j13kzx6z1my.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:32Z",
      "native_id": "2605.31391",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.31391"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.ins-det",
      "subfield": "applied-instrumentation"
    },
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Deep-learning-based low-energy trigger algorithms for the Hyper-Kamiokande experiment",
  "title_text": "Deep-learning-based low-energy trigger algorithms for the Hyper-Kamiokande experiment",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:28Z",
  "url": "https://research.draconitic.com/paper/80j13kzx6z1my",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1103/72mz-x21q",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}