We present the Data Quality Report Builder toolkit, DQRbuild, a suite of data quality tools that have been developed to vet gravitational-wave events in preparation for the fourth LIGO-Virgo-KAGRA observing run. We explain the main functionality and the many scientific tests that we support. To validate the performance of the tools included in the toolkit, we run a series of tests on all significant candidates shared as public alerts in the third observing run to compare against what was manually reported using human intervention. We find that these automated tools can now identify 96% of the problems identified by humans during this previous observing run, with a 24% false alarm rate. We conclude with a commentary on the prospects and potential challenges for fully automating the process of vetting the data quality for gravitational-wave events identified in future observing runs.
@article{arxiv.2605.16183,
title = {Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit},
author = {Derek Davis and Zach Yarbrough and Joseph Areeda and Ronaldas Macas and Nicolas Arnaud and Adrian Helmling-Cornell and Paolina Doliva and Olivia Godwin and Hirotaka Yuzurihara and Benjamin Mannix and Sofia Alvarez-Lopez and Max Trevor and Rachael Huxford and Philippe Nguyen and Beverly Berger and Chayan Chatterjee and Francesco Di Renzo and Christiano Palomba and Viola Sordini and Dimitrios Pesios and Marissa Walker and Airene Ahuja and Man Leong Chan and Julian Ding and Raymond Frey and Franz Herbst and Yannick Lecoeuche and Annudesh Liyanage and Jess McIver and Raymond Ng and Sophie Perry and Caitlin Rawcliffe and Robert Schofield},
journal= {arXiv preprint arXiv:2605.16183},
year = {2026}
}