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

TL;DR

This paper improves gravitational-wave data analysis by identifying new glitch classes using machine learning, detector monitoring, and citizen science, revealing prevalent ground motion-related noise affecting LIGO detectors.

Contribution

It introduces an updated glitch classification method combining citizen science and detector data, discovering a new ground motion-related glitch class and its impact on detector noise.

Findings

27% of transient noise at LIGO Livingston is from the new glitch class

Ground motion is a significant source of transient noise

Citizen science aids in discovering new glitch types

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. 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.