{
  "abstract": "We present a hardware-efficient hybrid trigger for FPGA-based radio detection of extensive air showers. The hybrid design consists of a lightweight denoiser that cleans raw ADC traces and a compact classifier that operates on the denoised output, enabling robust near-threshold pulse detection in high-interference environments. Both neural networks are trained quantization-aware. Signals are generated from detector-folded CoREAS/CORSIKA simulations and embedded into measured noise to form a realistic benchmark. The trigger reaches an AUC of 0.992 while fitting comfortably within the resource budget of a Zynq-7000 Z-7020, with microsecond-scale latency and sub-watt power consumption. RTL validation confirms agreement between the fixed-point hardware and the quantized software model, demonstrating that neural denoising combined with classification provides reliable, low-cost radio triggering in noisy environments.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.16973",
  "author_total": 10,
  "authors": [
    {
      "name": "Vesselin Dimitrov"
    },
    {
      "name": "Alperen Aksoy"
    },
    {
      "name": "Ilja Bekman"
    },
    {
      "name": "Markus Cristinziani"
    },
    {
      "name": "Eric-Teunis de Boone"
    },
    {
      "name": "Qader Dorosti"
    },
    {
      "name": "Chimezie Eguzo"
    },
    {
      "name": "Stefan Heidbrink"
    },
    {
      "name": "Stefan van Waasen"
    },
    {
      "name": "Andre Zambanini"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/00cwfgcy5yx3m.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.16973",
  "fulltext_url": null,
  "id": "00cwfgcy5yx3m",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.16973",
  "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",
      "astro-ph.IM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.16973",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/00cwfgcy5yx3m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:41Z",
      "native_id": "2609.16973",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.16973"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "Hardware-efficient neural networks for FPGA-based radio triggering of extensive air showers",
  "title_text": "Hardware-efficient neural networks for FPGA-based radio triggering of extensive air showers",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:41Z",
  "url": "https://research.draconitic.com/paper/00cwfgcy5yx3m",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.22323/1.538.0034",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}