Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning. Soni, S., Berry, C P L, Coughlin, S. B., Harandi, M., Jackson, C. B., Crowston, K., Østerlund, C, Patane, O., Katsaggelos, A. K., Trouille, L., Baranowski, V., Domainko, W. F., Kaminski, K., Rodriguez, M A L., Marciniak, U., Nauta, P., Niklasch, G., Rote, R. R., Téglás, B., Unsworth, C., & Zhang, C. Classical and Quantum Gravity, 38(19):195016, oct, 2021. ![Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and identify new glitch classes: fast scattering/crown and low-frequency blips. Using training sets assembled by monitoring of the state of the detector, and by citizen-science volunteers, we update the Gravity Spy machine-learning algorithm for glitch classification. We find that fast scattering/crown, linked to ground motion at the detector sites, is especially prevalent, and identify two subclasses linked to different types of ground motion. Reclassification of data based on the updated model finds that ∼27% of all transient noise at LIGO Livingston belongs to the fast scattering class, while ∼8% belongs to the low-frequency blip class, making them the most frequent and fourth most frequent sources of transient noise at that site. Our results demonstrate both how glitch classification can reveal potential improvements to gravitational-wave detectors, and how, given an appropriate framework, citizen-science volunteers may make discoveries in large data sets.
@article{Siddharth2021,
abstract = {The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and identify new glitch classes: fast scattering/crown and low-frequency blips. Using training sets assembled by monitoring of the state of the detector, and by citizen-science volunteers, we update the Gravity Spy machine-learning algorithm for glitch classification. We find that fast scattering/crown, linked to ground motion at the detector sites, is especially prevalent, and identify two subclasses linked to different types of ground motion. Reclassification of data based on the updated model finds that ∼27% of all transient noise at LIGO Livingston belongs to the fast scattering class, while ∼8% belongs to the low-frequency blip class, making them the most frequent and fourth most frequent sources of transient noise at that site. Our results demonstrate both how glitch classification can reveal potential improvements to gravitational-wave detectors, and how, given an appropriate framework, citizen-science volunteers may make discoveries in large data sets.},
archivePrefix = {arXiv},
arxivId = {2103.12104},
author = {Soni, S. and Berry, C P L and Coughlin, S. B. and Harandi, M. and Jackson, C. B. and Crowston, K. and {\O}sterlund, C and Patane, O. and Katsaggelos, A. K. and Trouille, L. and Baranowski, V-G and Domainko, W. F. and Kaminski, K. and Rodriguez, M A Lobato and Marciniak, U. and Nauta, P. and Niklasch, G. and Rote, R. R. and T{\'{e}}gl{\'{a}}s, B. and Unsworth, C. and Zhang, C.},
doi = {10.1088/1361-6382/ac1ccb},
eprint = {2103.12104},
issn = {0264-9381},
journal = {Classical and Quantum Gravity},
keywords = {LIGO,machine learning,neural network,noise classification,transient noise},
month = {oct},
number = {19},
pages = {195016},
title = {{Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning}},
url = {https://iopscience.iop.org/article/10.1088/1361-6382/ac1ccb},
volume = {38},
year = {2021}
}
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