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Title: A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences
Abstract The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies (O(1s)) 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.  more » « less
Award ID(s):
1922512
PAR ID:
10540655
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ;
Publisher / Repository:
Research Square
Date Published:
Format(s):
Medium: X
Institution:
Research Square
Sponsoring Org:
National Science Foundation
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