Automated software catches 96% of data quality problems in gravitational-wave alerts

Derek Davis, Zach Yarbrough, Joseph Areeda, Ronaldas Macas, Nicolas Arnaud, Adrian Helmling-Cornell, Paolina Doliva, Olivia Godwin, Hirotaka Yuzurihara, Benjamin Mannix, Sofia Alvarez-Lopez, Max Trevor, Rachael Huxford, Philippe Nguyen, Beverly Berger, Chayan Chatterjee, Francesco Di Renzo, Christiano Palomba, Viola Sordini, Dimitrios Pesios, Marissa Walker, Airene Ahuja, Man Leong Chan, Julian Ding, Raymond Frey, Franz Herbst, Yannick Lecoeuche, Annudesh Liyanage, Jess McIver, Raymond Ng, Sophie Perry, Caitlin Rawcliffe, Robert Schofield

Identifying a genuine gravitational-wave event requires checking whether detector noise, glitches, or environmental disturbances might have mimicked or contaminated a signal — work previously done by human experts under time pressure. The DQRbuild toolkit automates this vetting process and was benchmarked against every significant public alert from LIGO-Virgo-KAGRA's third observing run. It recovered 96% of the problems human reviewers flagged, with a 24% false-alarm rate on spurious issues. As detection rates increase with improved detector sensitivity, automation of this kind becomes essential for timely multi-messenger follow-up.