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

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 capa...

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Bibliographic Details
Published in:arXiv.org 2024-03
Main Authors: Marx, Ethan, Benoit, William, Gunny, Alec, Omer, Rafia, Chatterjee, Deep, Venterea, Ricco C, Wills, Lauren, Saleem, Muhammed, Moreno, Eric, Ryan Raikman, Govorkova, Ekaterina, Rankin, Dylan, Coughlin, Michael W, Harris, Philip, Katsavounidis, Erik
Format: Article
Language:English
Subjects:
Algorithms
Astronomy
Binary stars
Black holes
Gravitational waves
Machine learning
Neural networks
Online Access:Get full text
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Summary: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.
ISSN:2331-8422