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

TL;DR

This paper introduces a novel machine learning pipeline capable of detecting gravitational waves from compact binary coalescences in real-time, significantly reducing latency compared to traditional methods while maintaining high sensitivity.

Contribution

It presents the first fully machine learning-based low-latency pipeline for gravitational wave detection from CBCs, integrating neural networks into the astrophysics research ecosystem.

Findings

Achieves millisecond-scale inference latency.

Maintains state-of-the-art sensitivity to high-mass binary black holes.

Reduces detection latency compared to traditional methods.

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.