{
  "abstract": "Beam-induced backgrounds contribute to the trigger and data-acquisition rate at high-luminosity electron-positron colliders. Their dependence on beam and machine conditions motivates data-driven studies alongside simulation. A data-driven machine-learning method for trigger-level beam-background suppression is developed using experimental data recorded by BESIII. Compact neural-network classifiers are trained and evaluated for two trigger configurations: one based only on tracking and time-of-flight information, and one based only on electromagnetic-calorimeter information. Each classifier uses 48 binary Level-1 trigger conditions of the BESIII detector. The models distinguish hadron events from beam-background events and are evaluated against the established trigger selection at matched hadron-event efficiency. Relative to the established trigger selection, the beam-background false-positive rate is reduced by 20-60% across the studied configurations and center-of-mass energies, with stable suppression performance as tested using random-trigger data. Hardware-oriented synthesis indicates that the compact network is suitable for low-latency FPGA implementation. The proposed method can reduce the fraction of beam-background events in the trigger stream in future BESIII trigger upgrades. It also provides a reference for trigger optimization at future high-luminosity electron-positron colliders.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.26148",
  "author_total": 7,
  "authors": [
    {
      "name": "Hanlin Zhang"
    },
    {
      "name": "Minggang Zhao"
    },
    {
      "name": "Junhao Yin"
    },
    {
      "name": "Boping Chen"
    },
    {
      "name": "Jingzhou Zhao"
    },
    {
      "name": "Wenxuan Gong"
    },
    {
      "name": "Ke Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dnryavr3n8qwe.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.26148",
  "fulltext_url": null,
  "id": "dnryavr3n8qwe",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.26148",
  "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",
      "hep-ex"
    ],
    [
      "openalex",
      "3106|Particle Detector Development and Performance"
    ]
  ],
  "openalex": "W7222997027",
  "pdf_url": "https://arxiv.org/pdf/2610.26148",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dnryavr3n8qwe.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:52Z",
      "native_id": "2610.26148",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.26148"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Low-Latency Beam-Background Suppression for the BESIII Trigger System Using Machine Learning Trained on Experimental Data",
  "title_text": "Low-Latency Beam-Background Suppression for the BESIII Trigger System Using Machine Learning Trained on Experimental Data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:41Z",
  "url": "https://research.draconitic.com/paper/dnryavr3n8qwe",
  "venue": "arXiv",
  "versions": []
}