Rapid detection of gravitational waves from binary black hole mergers using sparse dictionary learning

Current gravitational wave (GW) detection pipelines for compact binary coalescence based on matched filtering have reported over 90 confident detections during the first three observing runs of the LIGO-Virgo-KAGRA (LVK) detector network. Decreasing the latency of detection, in particular, for future detectors anticipated to have high detection rates, remains an ongoing effort. In this paper, we develop and test a sparse dictionary learning (SDL) algorithm for the rapid detection of GWs. We evaluate the algorithms biases and estimate its GW detection rate for an astrophysical population of binary black holes. The SDL algorithm is assessed using both simulated data injected into the proposed <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mrow><a:mi mathvariant="normal">A</a:mi><a:mo>+</a:mo><a:mi>detector</a:mi></a:mrow></a:math> sensitivity and real data containing confident detections from the third LVK observing run. We find that our SDL algorithm can reconstruct a single binary black hole signal in less than 1 s. This suggests that SDL could be regarded as a promising approach for rapid, efficient GW detection in future observing runs of ground-based detectors.