Gravity Spy: Lessons Learned and a Path Forward

TL;DR

Gravity Spy combines citizen science and machine learning to classify and understand glitches in gravitational-wave data, improving detector performance and enabling discoveries of new glitch types.

Contribution

It introduces an integrated framework of citizen science and machine learning for glitch classification and discovery in gravitational-wave data.

Findings

Machine learning provides rapid initial classifications.

Volunteer classifications verify and improve machine learning models.

The project enables discovery of new glitch classes.

Abstract

The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine-learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine-learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine-learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.