{
  "abstract": "With the projected increase in the detection rate of compact-binary coalescences in the coming decade, there is critical need to develop fast, robust, and scalable alternatives to matched filtering for gravitational-wave searches. Transformer models have revolutionized natural language and audio processing but their application to gravitational-wave astronomy is still largely unexplored. In this work, we introduce \\castor, a transformer-based coincident search pipeline for detecting binary black hole gravitational-wave signals from Advanced LIGO detectors. One of the major features of our model is that it allows the false-alarm rate to be estimated via time slides cheaply without requiring repeated evaluations of the neural network. We evaluate \\castor\\ on datasets from the Machine-Learning Gravitational-Wave Search Challenge (MLGWSC-1) and on approximately five months of real O3b observing strain. When tested on benchmark datasets, \\castor\\ ranks among the most sensitive machine-learning pipelines and successfully recovers the majority of confident events from the GWTC-3 catalog that lie within its training range. We also benchmark \\castor\\ against another transformer architecture, GW-Whisper, a domain-adaptation of OpenAI's audio foundation model. We find that \\castor\\ substantially outperforms the repurposed audio model in sensitivity and also reduces the computational cost of background estimation by a factor of 20. Our results demonstrate a highly practical, scalable approach for deep-learning gravitational wave searches and empirical background estimation for future observing runs.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.00339",
  "author_total": 6,
  "authors": [
    {
      "name": "Chayan Chatterjee"
    },
    {
      "name": "Abigail Petulante"
    },
    {
      "name": "Haowei Fu"
    },
    {
      "name": "Yang Hu"
    },
    {
      "name": "Roy Lau"
    },
    {
      "name": "Karan Jani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a6v64yq49svtk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.00339",
  "fulltext_url": null,
  "id": "a6v64yq49svtk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.00339",
  "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.GA"
    ],
    [
      "arxiv",
      "gr-qc"
    ],
    [
      "openalex",
      "3103|Pulsars and Gravitational Waves Research"
    ]
  ],
  "openalex": "W7206198968",
  "pdf_url": "https://arxiv.org/pdf/2609.00339",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/a6v64yq49svtk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:11Z",
      "native_id": "2609.00339",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.00339"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.GA",
      "subfield": "galaxies"
    },
    {
      "field": "physics",
      "origin": "arxiv:gr-qc",
      "subfield": "gravitation"
    }
  ],
  "title": "A Fast and Scalable Transformer Pipeline for Binary Black Hole Detection",
  "title_text": "A Fast and Scalable Transformer Pipeline for Binary Black Hole Detection",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:11Z",
  "url": "https://research.draconitic.com/paper/a6v64yq49svtk",
  "venue": "arXiv",
  "versions": []
}