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

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. 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 a new glitch class linked to ground motion at the detector sites is especially prevalent, and identify two subclasses of this 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 new glitch class, making it the most frequent source 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.