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.
@article{arxiv.2208.12849,
title = {Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications},
author = {J. Glanzer and S. Banagiri and S. B. Coughlin and S. Soni and M. Zevin and C. P. L. Berry and O. Patane and S. Bahaadini and N. Rohani and K. Crowston and V. Kalogera and C. Østerlund and A. Katsaggelos},
journal= {arXiv preprint arXiv:2208.12849},
year = {2023}
}
Comments
33 pages (including bibliography), 11 figures, 3 tables, 1 appendix; updated to match version to be published in Classical & Quantum Gravity; data release available from https://doi.org/10.5281/zenodo.5649211