Gravity Spy: lessons learned and a path forward
Name
13360_2023_Article_4795.pdf
Size
1.79 MB
Format
Adobe PDF
Checksum (MD5)
0009416a0366585a432a51b86e0e3d4a
Author(s)
Zevin, Michael
Jackson, Corey B.
Doctor, Zoheyr
Wu, Yunan
Østerlund, Carsten
Johnson, L. C.
Berry, Christopher P. L.
Crowston, Kevin
Coughlin, Scott B.
Kalogera, Vicky
Date Issued
January 30, 2024
Publisher
Springer Berlin Heidelberg
Citation
The European Physical Journal Plus. 2024 Jan 30;139(1):100
Version
Final published version
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.
MIT Department
LIGO (Observatory : Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1140/epjp/s13360-023-04795-4
