GW-YOLO: Multi-transient segmentation in LIGO using computer vision

Abstract

Time series data and their time-frequency representations from gravitational-wave interferometers present opportunities for artificial intelligence methods in signal and image processing, particularly for low-latency analysis. In this work, we introduce GW-YOLO, a signal and noise identification tool based on the YOLO (You Only Look Once) object detection framework. GW-YOLO identifies whether an observed transient contains noise, an astrophysical signal, or both, while providing time-frequency coordinates of detected objects through pixel-level segmentation masks. Our approach achieves a 53% detection efficiency for binary black hole signals in the signal-to-noise ratio (SNR) 12--15 range when they overlap with transient noise, increasing to more than 75% at SNR 15--18. For binary neutron star signals overlapping with transient noise, the detection efficiency reaches 57% at SNR 30--33 and 87% at SNR 39--42. To our knowledge, this is the first quantitative assessment of the ability to detect astrophysical signals overlapping with realistic instrumental noise in gravitational-wave interferometers. We also present a fully automated, low-latency pipeline that produces pixel-level segmentation masks for individual noise transients and astrophysical signals, enabling further automation of event validation and downstream noise-mitigation procedures.