{
  "abstract": "Data acquisition (DAQ) systems at future particle physics experiments stand to benefit from the extremes of AI/ML development: large-scale foundation models can enhance the performance of feature extraction algorithms, and small-scale on-detector deployments can enable real-time intelligent data handling. This work provides the first fine-tuning of an industrial foundation model for particle physics DAQ. Starting from the backbone of Google Research's TimesFM (Time Series Foundation Model), we demonstrate fine-tuning on real-time regression tasks for drift chamber trackers and dual-readout calorimeters. Furthermore, the fine-tuned TimesFM model is distilled into a student and co-designed with FPGA implementation to enable these models to run in real-time at future colliders. The fine-tuned distillations meet or exceed the performance of previously published AI/ML solutions for each task. Further, the pipeline of distillation and model compression from TimesFM is generic and can be easily adapted to a variety of 1D waveform tasks across domains.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.23385",
  "author_total": 4,
  "authors": [
    {
      "name": "Gia Ancone"
    },
    {
      "name": "Qibin Liu"
    },
    {
      "name": "Liangyu Wu"
    },
    {
      "name": "Julia Gonski"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5q228n3nstmba.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.23385",
  "fulltext_url": null,
  "id": "5q228n3nstmba",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.23385",
  "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",
      "3106|Particle Detector Development and Performance"
    ]
  ],
  "openalex": "W7214032533",
  "pdf_url": "https://arxiv.org/pdf/2609.23385",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5q228n3nstmba.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:19Z",
      "native_id": "2609.23385",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.23385"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.ins-det",
      "subfield": "applied-instrumentation"
    },
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Leveraging Industrial Foundation Models at the Edge of Particle Physics Detectors via Distillation Learning and Hardware Co-design",
  "title_text": "Leveraging Industrial Foundation Models at the Edge of Particle Physics Detectors via Distillation Learning and Hardware Co-design",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:19Z",
  "url": "https://research.draconitic.com/paper/5q228n3nstmba",
  "venue": "arXiv",
  "versions": []
}