{
  "abstract": "Binary pulsars provide valuable laboratories for testing theories of gravity, but orbital Doppler shifts complicate their detection. Fourier-domain acceleration and jerk searches address this challenge via matched filtering, but at substantial computational cost. We present LeoNet, a convolutional neural network that uses ten learnable filters to extract features of signals affected by Doppler shifts. The resulting ten-channel feature map provides a compact, lower-dimensional alternative to an explicitly sampled acceleration-jerk response grid and is analysed by a convolutional classifier to identify candidate signals. For simulated observations lasting 500 s, LeoNet achieves a mean relative reduction in false negative rate of 55.5% across five sampling intervals compared with the evaluated PRESTO acceleration-search configuration. TensorRT-optimised LeoNet processes each 500 s observation in 3.44-4.37 ms in FP32 on an NVIDIA H100 PCIe GPU across eight sampling intervals, including preprocessing, inference, and postprocessing. At a sampling interval of 128 microseconds, its mean processing time is 3.54 ms, compared with 1.767 s for PRESTO FDAS on an AMD EPYC 9825 CPU with search-frequency limits of 96-1000 Hz, corresponding to an approximately 499-fold speedup in the measured processing time. These results suggest that LeoNet has the potential to improve detection performance, while its millisecond-scale processing time supports its use as a candidate-identification stage in real-time binary pulsar search pipelines.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.00908",
  "author_total": 6,
  "authors": [
    {
      "name": "Zhaocheng Gong"
    },
    {
      "name": "Jack White"
    },
    {
      "name": "Zeyu Yang"
    },
    {
      "name": "Jayanta Roy"
    },
    {
      "name": "Karel Adamek"
    },
    {
      "name": "Wesley Armour"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ehj3280khaj3k.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.00908",
  "fulltext_url": null,
  "id": "ehj3280khaj3k",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.00908",
  "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",
      "astro-ph.IM"
    ],
    [
      "openalex",
      "3103|Pulsars and Gravitational Waves Research"
    ]
  ],
  "openalex": "W7215698186",
  "pdf_url": "https://arxiv.org/pdf/2610.00908",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ehj3280khaj3k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:03Z",
      "native_id": "2610.00908",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.00908"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    }
  ],
  "title": "LeoNet: A Machine Learning Method for Binary Pulsar Classification",
  "title_text": "LeoNet: A Machine Learning Method for Binary Pulsar Classification",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:03Z",
  "url": "https://research.draconitic.com/paper/ehj3280khaj3k",
  "venue": "arXiv",
  "versions": []
}