Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications. Glanzer, J., Banagiri, S, Coughlin, S B, Soni, S., Zevin, M., Berry, C. P. L., Patane, O., Bahaadini, S., Rohani, N., Crowston, K., Kalogera, V, Østerlund, C., Trouille, L., & Katsaggelos, A Classical and Quantum Gravity, 40(6):065004, mar, 2023. ![Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 3 downloads 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.
@article{Jane2022,
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.},
archivePrefix = {arXiv},
arxivId = {2208.12849},
author = {Glanzer, Jane and Banagiri, S and Coughlin, S B and Soni, Siddharth and Zevin, Michael and Berry, Christopher Philip Luke and Patane, Oli and Bahaadini, Sara and Rohani, Neda and Crowston, Kevin and Kalogera, V and {\O}sterlund, Carsten and Trouille, Laura and Katsaggelos, A},
doi = {10.1088/1361-6382/acb633},
eprint = {2208.12849},
issn = {0264-9381},
journal = {Classical and Quantum Gravity},
month = {mar},
number = {6},
pages = {065004},
title = {{Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications}},
url = {https://iopscience.iop.org/article/10.1088/1361-6382/acb633},
volume = {40},
year = {2023}
}
Downloads: 3
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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. 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