Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit
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.
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Authors: Derek Davis, Z. Yarbrough, R. Macas, Nicolas Arnaud, Adrian F. Helmling-Cornell, Paolina Doliva, P. Godwin, S. Álvarez-López, C. Chatterjee, F. Di Renzo, Dimitrios Pesios, J. S. Areeda, M. Walker, H. Yuzurihara, Benjamin Mannix, M. Trevor, P. Nguyen, V. Sordini, CRISTIANO PALOMBA, B. K. Berger, R. Huxford
Institutions: Vanderbilt University, Stanford University, Centre National de la Recherche Scientifique, Massachusetts Institute of Technology, University of Maryland, College Park, Sapienza University of Rome, Lyon 1 Université, Pennsylvania State University, Aristotle University of Thessaloniki, University of Portsmouth, Louisiana State University, California Institute of Technology, Bard College, California State University, Fullerton, University of Rhode Island, Christopher Newport University, Institute of Nuclear Physics of Lyon