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

J. Glanzer, S. Banagiri, S. B. Coughlin, S. Soni, M. Zevin, Christopher Philip Luke Berry*, O. Patane, S. Bahaadini, N. Rohani, K. Crowston, V. Kalogera, C. Østerlund, L. Trouille, A. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

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 laser interferometric gravitational-wave observatory (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.

Original languageEnglish (US)
Article number065004
JournalClassical and Quantum Gravity
Volume40
Issue number6
DOIs
StatePublished - Mar 16 2023

Keywords

  • Gravity Spy
  • LIGO
  • glitches
  • gravitational waves
  • machine learning

ASJC Scopus subject areas

  • Physics and Astronomy (miscellaneous)

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