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The Gravity Spy project aims to uncover the origins of glitches, transientbursts of noise that hamper analysis of gravitational-wave data. By using boththe work of citizen-science volunteers and machine-learning algorithms, theGravity Spy project enables reliable classification of glitches. Citizenscience and machine learning are intrinsically coupled within the Gravity Spyframework, with machine-learning classifications providing a rapid first-passclassification of the dataset and enabling tiered volunteer training, andvolunteer-based classifications verifying the machine classifications,bolstering the machine-learning training set and identifying new morphologicalclasses of glitches. These classifications are now routinely used in studiescharacterizing the performance of the LIGO gravitational-wave detectors.Providing the volunteers with a training framework that teaches them toclassify a wide range of glitches, as well as additional tools to aid theirinvestigations of interesting glitches, empowers them to make discoveries ofnew classes of glitches. This demonstrates that, when giving suitable support,volunteers can go beyond simple classification tasks to identify new featuresin data at a level comparable to domain experts. The Gravity Spy project is nowproviding volunteers with more complicated data that includes auxiliarymonitors of the detector to identify the root cause of glitches.

Gravity Spy: Lessons Learned and a Path Forward