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Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications

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arxiv 2208.12849 v2 pith:BNAWTFGQ submitted 2022-08-26 gr-qc astro-ph.IM

J. Glanzer , S. Banagiri , S. B. Coughlin , S. Soni , M. Zevin , C. P. L. Berry , O. Patane , S. Bahaadini
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N. Rohani K. Crowston V. Kalogera C. {O}sterlund A. Katsaggelos
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classification gr-qcastro-ph.IM
keywords glitchesligodatagravitational-wavegravityadvancedclassifynoise
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Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time-frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of Advanced LIGO. We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. GW240925 and GW250207: Astrophysical Calibration of Gravitational-wave Detectors

    gr-qc 2026-05 unverdicted novelty 8.0 of 10

    The first informative astrophysical calibration of gravitational-wave detectors is reported using GW240925 and GW250207.

  2. Scattered light noise at LIGO Livingston Observatory during O4

    astro-ph.IM 2026-05 accept novelty 5.0 of 10

    Scattered light glitches at LIGO Livingston during O4 were traced to microseismic and high-frequency ground motions and mitigated by baffles and seismic isolation.

  3. Hunting for new glitches in LIGO data using community science

    gr-qc 2025-08 unverdicted novelty 4.0 of 10

    Volunteers propose new glitch categories in LIGO data that connect to instrument states and pose difficulties for existing ML glitch classifiers.

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