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A Fast and Scalable Transformer Pipeline for Binary Black Hole Detection — arXiv:2609.00339

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.00339
License (as archived): CC0-1.0
Archived: 2026-10-17T07:31:37.866Z
SHA-256 of the archived text: 3a97e16f1c967ccda801cecdf03267d4cb20b8c5941b362addf99aee05fd3f96
Stable URL of this page: https://metraipolis.com/documento/dc_23aec7901549

How to cite

A Fast and Scalable Transformer Pipeline for Binary Black Hole Detection — arXiv:2609.00339 — arXiv (en), https://arxiv.org/abs/2609.00339 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_23aec7901549 (Metraipolis archive dc_23aec7901549, sha256 3a97e16f1c967ccda801cecdf03267d4cb20b8c5941b362addf99aee05fd3f96)

BibTeX

@misc{dc23aec7901549,
  title = {A Fast and Scalable Transformer Pipeline for Binary Black Hole Detection — arXiv:2609.00339},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.00339},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_23aec7901549; sha256 3a97e16f1c967ccda801cecdf03267d4cb20b8c5941b362addf99aee05fd3f96},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.00339. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: fisica e astronomia (retro-catalogo arXiv: annunci del 2026-09-02 in gr-qc).
Categorie arXiv: General Relativity and Quantum Cosmology (gr-qc) ; Astrophysics of Galaxies (astro-ph.GA).
Pagina del lavoro: https://arxiv.org/abs/2609.00339 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T07:20:32.092Z dalla pagina https://arxiv.org/catchup/gr-qc/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).