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

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.

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Publication Details

Journal
Classical and Quantum Gravity
Published
2026-09-21
DOI
https://doi.org/10.1088/1361-6382/aeaa9d
Primary Topic
Pulsars and Gravitational Waves Research
Type
article
Field-Weighted Citation Impact
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article

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

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Classical and Quantum Gravity
Pulsars and Gravitational Waves Research
article

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

B. K. Berger, J. S. Areeda, C. Chatterjee, Dimitrios Pesios, Benjamin Mannix, S. Álvarez-López, CRISTIANO PALOMBA, R. Huxford, P. Godwin, Z. Yarbrough, M. Trevor, M. Walker, P. Nguyen, R. Macas, Adrian F. Helmling-Cornell, F. Di Renzo, V. Sordini, H. Yuzurihara, Derek Davis, Paolina Doliva, Nicolas Arnaud
article en

Abstract

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.

Classical and Quantum Gravity
Université Claude Bernard Lyon 1 (FR), Louisiana State University (US), California Institute of Technology (US), Centre National de la Recherche Scientifique (FR), Pennsylvania State University (US), California State University, Fullerton (US), University of Rhode Island (US), Christopher Newport University (US), Vanderbilt University (US), Aristotle University of Thessaloniki (GR), Bard College (US), Institute of Nuclear Physics of Lyon (FR), University of Portsmouth (GB), Massachusetts Institute of Technology (US), University of Maryland, College Park (US), Sapienza University of Rome (IT), Stanford University (US)
Openalex Percentile: Top 56%
Pulsars and Gravitational Waves Research
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