Gravity Spy: lessons learned and a path forward

Michael Zevin, Corey B. Jackson, Zoheyr Doctor, Yunan Wu, Carsten Østerlund, L. Clifton Johnson, Christopher P.L. Berry, Kevin Crowston, Scott B. Coughlin, Vicky Kalogera, Sharan Banagiri, Derek Davis, Jane Glanzer, Renzhi Hao, Aggelos K. Katsaggelos, Oli Patane, Jennifer Sanchez, Joshua Smith, Siddharth Soni, Laura TrouilleMarissa Walker, Irina Aerith, Wilfried Domainko, Victor Georges Baranowski, Gerhard Niklasch, Barbara Téglás

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number100
JournalEuropean Physical Journal Plus
Volume139
Issue number1
DOIs
StatePublished - Jan 2024

ASJC Scopus subject areas

  • General Physics and Astronomy
  • Fluid Flow and Transfer Processes

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