Eur. Phys. J. Plus (2024) 139: 100
https://doi.org/10.1140/epjp/s13360-023-04795-4

Regular Article

Gravity Spy: lessons learned and a path forward

¹ Kavli Institute for Cosmological Physics, The University of Chicago, 5640 South Ellis Avenue, 60637, Chicago, IL, USA
² Enrico Fermi Institute, The University of Chicago, 933 East 56th Street, 60637, Chicago, IL, USA
³ Information School, University of Wisconsin–Madison, 600 N Park Street, 53706, Madison, WI, USA
⁴ Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), Northwestern University, 1800 Sherman Ave, 60201, Evanston, IL, USA
⁵ Department of Electrical and Computer Engineering, Northwestern University, 2145 Sheridan Road, 60208, Evanston, IL, USA
⁶ School of Information Studies, Syracuse University, 343 Hinds Hall, 13210, Syracuse, NY, USA
⁷ Zooniverse, The Adler Planetarium, 1300 South DuSable Lake Shore Drive, 60605, Chicago, IL, USA
⁸ SUPA, School of Physics and Astronomy, University of Glasgow, Kelvin Building, University Ave, G12 8QQ, Glasgow, UK
⁹ Department of Physics and Astronomy, Northwestern University, 2145 Sheridan Road, 60208, Evanston, IL, USA
¹⁰ LIGO Laboratory, California Institute of Technology, 1200 East California Boulavard, 91125, Pasadena, CA, USA
¹¹ Department of Physics and Astronomy, Louisiana State University, 202 Nicholson Hall, 70803, Baton Rouge, LA, USA
¹² LIGO Hanford Observatory, 127124 N Route 10, 99354, Hanford, WA, USA
¹³ The Nicholas and Lee Begovich Center for Gravitational-Wave Physics and Astronomy, California State University Fullerton, 800 N. State College Blvd, 92831, Fullerton, CA, USA
¹⁴ LIGO Laboratory, Massachusetts Institute of Technology, 185 Albany Street, 02139, Cambridge, MA, USA
¹⁵ Department of Physics, Computer Science and Engineering, Christopher Newport University, One Avenue of the Arts, 23606, Newport News, VA, USA
¹⁶ Sorbonne Paris Nord University, 99 Avenue Jean Baptiste Clément, 93430, Villetaneuse, France
¹⁷ ConSol Software GmbH, St.-Cajetan-Strasse 43, 81669, Munich, Germany
¹⁸ , Cedar Falls, Iowa, USA
¹⁹ , Hermagor, Austria
²⁰ , Budapest, Hungary

^a mzevin@adlerplanetarium.org

Received: 24 August 2023
Accepted: 13 December 2023
Published online: 30 January 2024

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.