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

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.