Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit

TL;DR

The paper introduces the Data Quality Report Builder toolkit (DQRbuild), an automated suite for vetting gravitational-wave events, achieving high accuracy in identifying data issues compared to manual methods.

Contribution

Development and validation of DQRbuild, a toolkit that automates data quality assessment for gravitational-wave events, matching human performance in identifying issues.

Findings

Automated tools identify 96% of issues found by humans.

The toolkit has a 24% false alarm rate.

Validated on third observing run data.

Abstract

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.