TY - JOUR T1 - Gravity Spy: Lessons Learned and a Path Forward JF - European Physical Journal Plus Y1 - 2024 A1 - Michael Zevin A1 - Corey B. Jackson A1 - Zoheyr Doctor A1 - Yunan Wu A1 - Carsten Østerlund A1 - L. Clifton Johnson A1 - Christopher P. L. Berry A1 - Kevin Crowston A1 - Scott B. Coughlin A1 - Vicky Kalogera A1 - Sharan Banagiri A1 - Derek Davis A1 - Jane Glanzer A1 - Renzhi Hao A1 - Aggelos K. Katsaggelos A1 - Oli Patane A1 - Jennifer Sanchez A1 - Joshua Smith A1 - Siddharth Soni A1 - Laura Trouille A1 - Marissa Walker A1 - Irina Aerith A1 - Wilfried Domainko A1 - Victor-Georges Baranowski A1 - Gerhard Niklasch A1 - Barbara Téglás AB -

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.

VL - 139 ER - TY - JOUR T1 - Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning JF - Classical and Quantum Gravity Y1 - 2021 A1 - S Soni A1 - C P L Berry A1 - S B Coughlin A1 - M Harandi A1 - C B Jackson A1 - K Crowston A1 - C Østerlund A1 - O Patane A1 - A K Katsaggelos A1 - L Trouille A1 - V-G Baranowski A1 - W F Domainko A1 - K Kaminski A1 - M A Lobato Rodriguez A1 - U Marciniak A1 - P Nauta A1 - G Niklasch A1 - R R Rote A1 - B Téglás A1 - C Unsworth A1 - C Zhang VL - 38 IS - 19 ER -