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

Understand

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.