{
  "abstract": "This work presents two hardware-accelerated strategies for the upgrade of the CMS Level-1 Trigger (L1T) to target efficiency losses due to highly energetic radiating muons and unconventional physics signatures, such as Long-Lived Particles (LLPs). Operating directly on low-level detector hits, the first strategy implements a dedicated muon shower identification algorithm in the CMS barrel region that monitors hit multiplicities in the Drift Tube chambers to generate Shower Primitives. Executing with sub-bunch-crossing latency and ultra-low resource consumption, this algorithm successfully tags showered muons and provides essential context for downstream track finders. The second approach targets the barrel-endcap transition (overlap) region by evaluating Graph Neural Networks to reconstruct displaced muon tracks. Following a divided methodology that first isolates hardware feasibility from evolving physics refinements, an initial proxy model based on a GraphSAGE architecture is evaluated to validate its viability on FPGAs within strict latency limits. This implementation utilizes an INT8-PO2 quantization technique coupled with a data-driven bit-width optimization. By replacing DSP-heavy fixed-point multiplications with fast compile-time arithmetic bit-shifts and allocating the minimum required bits per signal, this hardware-software co-design reduces DSP utilization to $20\\%$ and achieves a deterministic inference latency of just $19$ clock cycles. Together, these developments pave the way for triggering on non-standard muon signatures, such as those from LLP decays, at L1T.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.06379",
  "author_total": 6,
  "authors": [
    {
      "name": "Daniel Estrada"
    },
    {
      "name": "Elena Aller"
    },
    {
      "name": "Andrea Cardini"
    },
    {
      "name": "Santiago Folgueras"
    },
    {
      "name": "Pelayo Leguina"
    },
    {
      "name": "Javier Prado"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9f3sthdzpkdzy.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.06379",
  "fulltext_url": null,
  "id": "9f3sthdzpkdzy",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.06379",
  "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",
      "hep-ex"
    ],
    [
      "arxiv",
      "physics.ins-det"
    ],
    [
      "openalex",
      "3106|Particle Detector Development and Performance"
    ]
  ],
  "openalex": "W7220682472",
  "pdf_url": "https://arxiv.org/pdf/2610.06379",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9f3sthdzpkdzy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:01Z",
      "native_id": "2610.06379",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.06379"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.ins-det",
      "subfield": "applied-instrumentation"
    },
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "FPGA-based muon shower identification and graph neural network tracking algorithms for HL-LHC triggers",
  "title_text": "FPGA-based muon shower identification and graph neural network tracking algorithms for HL-LHC triggers",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:01Z",
  "url": "https://research.draconitic.com/paper/9f3sthdzpkdzy",
  "venue": "arXiv",
  "versions": []
}