Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning

Soni, S. et al. (2021) Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning. Classical and Quantum Gravity, 38(19), 195016. (doi: 10.1088/1361-6382/ac1ccb)

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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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Berry, Dr Christopher
Authors: 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.-G., Domainko, W. F., Kaminski, K., Lobato Rodriguez, M. A., Marciniak, U., Nauta, P., Niklasch, G., Rote, R. R., Téglás, B., Unsworth, C., and Zhang, C.
College/School:College of Science and Engineering > School of Physics and Astronomy
Journal Name:Classical and Quantum Gravity
Publisher:IOP Publishing
ISSN:0264-9381
ISSN (Online):1361-6382
Published Online:11 August 2021
Copyright Holders:Copyright © 2021 IOP Publishing Ltd
First Published:First published in Classical and Quantum Gravity 38(19): 195016
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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