{
  "abstract": "Autonomous radio self-triggering for extensive air showers requires strong rejection of radio-frequency interference while preserving weak pulses within station-level hardware constraints. We present a hybrid neural trigger comprising a compact waveform denoiser followed by a classifier. The method is evaluated using experimentally measured high-interference background traces and detector-folded simulated air-shower pulses produced with the Pierre Auger Offline chain, with the benchmark concentrated in the near-threshold regime. Hardware constraints are incorporated through hyperparameter optimisation, quantisation-aware training, and high-granularity fixed-point quantisation. Applying the conventional peak-envelope trigger after denoising increases its area under the receiver-operating-characteristic curve from 0.63 to 0.98. At a false-positive rate of 10^-4, the full denoiser-classifier chain retains about 41% of the held-out signal traces, compared with 27% for the classifier acting on the raw waveform and about 2% for the peak-envelope reference. The fixed-point firmware is synthesised, placed, and routed on representative field-programmable gate array targets, where it achieves timing closure with microsecond-scale latency and compact arithmetic-resource usage. These results establish hybrid neural denoising as a practical FPGA-compatible route toward radio-only triggering of weak and inclined air-shower signals in noisy environments.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.28332",
  "author_total": 10,
  "authors": [
    {
      "name": "Qader Dorosti"
    },
    {
      "name": "Alperen Aksoy"
    },
    {
      "name": "Ilja Bekman"
    },
    {
      "name": "Markus Cristinziani"
    },
    {
      "name": "Eric-Teunis de Boone"
    },
    {
      "name": "Vesselin Dimitrov"
    },
    {
      "name": "Chimezie Eguzo"
    },
    {
      "name": "Stefan Heidbrink"
    },
    {
      "name": "Stefan van Waasen"
    },
    {
      "name": "Andre Zambanini"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dfteeqk88dgb9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.28332",
  "fulltext_url": null,
  "id": "dfteeqk88dgb9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.28332",
  "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"
    ],
    [
      "arxiv",
      "hep-ex"
    ],
    [
      "openalex",
      "3103|Radio Astronomy Observations and Technology"
    ]
  ],
  "openalex": "W7162675188",
  "pdf_url": "https://arxiv.org/pdf/2605.28332",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dfteeqk88dgb9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:48Z",
      "native_id": "2605.28332",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.28332"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Hybrid neural denoising for resource-efficient near- and sub-threshold radio triggering of extensive air showers",
  "title_text": "Hybrid neural denoising for resource-efficient near- and sub-threshold radio triggering of extensive air showers",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:48Z",
  "url": "https://research.draconitic.com/paper/dfteeqk88dgb9",
  "venue": "arXiv",
  "versions": []
}