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, M. Saleem, Eric Moreno, Ryan Raikman, Ekaterina Govorkova, Malina Desai, J. Krupa, Dylan Rankin, M. W. Coughlin, Philip Harris, E. Katsavounidis
Abstract
The promise of multimessenger astronomy relies on the rapid detection of gravitational waves at very low latencies [$\mathcal{O}(1\text{ }\text{ }\mathrm{s})$] in order to maximize the amount of time available for follow-up observations. In recent years, neural networks have demonstrated robust nonlinear 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 nontrivial. Here, we present a machine-learning-based pipeline for the detection of gravitational waves from compact binary coalescences designed to run 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.