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

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

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
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classification gr-qc astro-ph.IM
keywords binarydetectiongravitationalpipelinewavesalgorithmscoalescencescompact
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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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