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A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

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arxiv 2403.18661 v2 pith:5UO7CRWW submitted 2024-03-27 gr-qc astro-ph.IM

Ethan Marx , William Benoit , Alec Gunny , Rafia Omer , Deep Chatterjee , Ricco C. Venterea , Lauren Wills , Muhammed Saleem
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Eric Moreno Ryan Raikman Ekaterina Govorkova Dylan Rankin Michael W. Coughlin Philip Harris Erik Katsavounidis
This is my paper
classification gr-qcastro-ph.IM
keywords binarydetectiongravitationalpipelinewavesalgorithmscoalescencescompact
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies ($\mathcal{O}$(1\,s)) in order to maximize the amount of time available for follow-up observations. In recent years, neural-networks have demonstrated robust non-linear modeling capabilities and millisecond-scale inference at a comparatively small computational footprint, making them an attractive family of algorithms in this context. However, integration of these algorithms into the gravitational-wave astrophysics research ecosystem has proven non-trivial. Here, we present the first fully machine learning-based pipeline for the detection of gravitational waves from compact binary coalescences (CBCs) running in low-latency. We demonstrate this pipeline to have a fraction of the latency of traditional matched filtering search pipelines while achieving state-of-the-art sensitivity to higher-mass stellar binary black holes.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Flexible Gravitational-Wave Parameter Estimation with Transformers

    gr-qc 2025-12 conditional novelty 6.0 of 10

    Dingo-T1 is one transformer model that adapts at inference to arbitrary detector subsets and frequency cuts for gravitational-wave parameter estimation.

  2. Improving gravitational wave search sensitivity with TIER: Trigger Inference using Extended strain Representation

    gr-qc 2025-07 conditional novelty 6.0 of 10

    A machine learning classifier trained on the extended noise environment around gravitational wave candidates improves search sensitivity for heavy, unequal-mass black hole mergers by up to roughly 20 percent.

  3. Searching for binary black hole mergers with deep learning in Advanced LIGO's third observing run

    gr-qc 2025-12 conditional novelty 5.0 of 10

    A hybrid matched-filter/deep-learning pipeline recovers 31 known O3 events and reports a new tentative high-mass candidate, with sensitivity comparable to existing searches only for chirp masses above 25 solar masses.

  4. Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms

    gr-qc 2025-09 conditional novelty 5.0 of 10

    AresGW model 1's injection detection count at a false-alarm rate of 1/month varies with noise dataset by up to 39% coefficient of variation, while sensitive distance varies by only a few percent.

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