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Published November 15, 2022 | Published
Journal Article Open

Curious case of GW200129: Interplay between spin-precession inference and data-quality issues


Measurement of spin-precession in black hole binary mergers observed with gravitational waves is an exciting milestone as it relates to both general relativistic dynamics and astrophysical binary formation scenarios. In this study, we revisit the evidence for spin-precession in GW200129 and localize its origin to data in LIGO Livingston in the 20–50 Hz frequency range where the signal amplitude is lower than expected from a nonprecessing binary given all the other data. These data are subject to known data quality issues as a glitch was subtracted from the detector's strain data. The lack of evidence for spin-precession in LIGO Hanford leads to a noticeable inconsistency between the inferred binary mass ratio and precessing spin in the two LIGO detectors, something not expected from solely different Gaussian noise realizations. We revisit the LIGO Livingston glitch mitigation and show that the difference between a spin-precessing and a nonprecessing interpretation for GW200129 is smaller than the statistical and systematic uncertainty of the glitch subtraction, finding that the support for spin-precession depends sensitively on the glitch modeling. We also investigate the signal-to-noise ratio ∼ 7 trigger in the less sensitive Virgo detector. Though not influencing the spin-precession studies, the Virgo trigger is grossly inconsistent with the ones in LIGO Hanford and LIGO Livingston as it points to a much heavier system. We interpret the Virgo data in the context of further data quality issues. While our results do not disprove the presence of spin-precession in GW200129, we argue that any such inference is contingent upon the statistical and systematic uncertainty of the glitch mitigation. Our study highlights the role of data quality investigations when inferring subtle effects such as spin-precession for short signals such as the ones produced by high-mass systems.

Additional Information

© 2022 American Physical Society. We thank Aaron Zimmerman, Eric Thrane, Paul Lasky, Hui Tong, Geraint Pratten, Mark Hannam, Charlie Hoy, Jonathan Thompson, Steven Fairhurst, Vivien Raymond, Max Isi, and Colm Talbot for useful discussions. We also thank Vijay Varma for providing a version of lalsuite optimized for running nrsur7dq4, as well as suggestions for some of our configuration settings. This research has made use of data, software and/or web tools obtained from the Gravitational Wave Open Science Center [60], a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale della Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by Polish and Hungarian institutes. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by NSF Grants No. PHY-0757058 and No. PHY-0823459. This research utilized the OzStar Supercomputing Facility at Swinburne University of Technology. The OzStar facility is partially funded by the Astronomy National Collaborative Research Infrastructure Strategy (NCRIS) allocation provided by the Australian Government. This research was enabled in part by computing resources provided by Simon Fraser University and the Digital Research Alliance of Canada [61]. S. H. and K. C. were supported by NSF Grant No. PHY-2110111. Software: gwpy [45], matplotlib [62], numpy [63], pandas [64,65], scipy [66], qnm [38], surfinbh [39], bilby [67], lalsuite [68], bayeswave [69].

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Published - PhysRevD.106.104017.pdf


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August 20, 2023
October 18, 2023