- Gravity Spy is an NSF-funded interdisciplinary project incorporating citizen science, machine learning, social science, and aLIGO detector characterization
- One major issue afflicting aLIGO’s ability to detect gravitational waves is poorly-modeled noise known as “glitches”
- Gravity Spy will aid in the classification and characterization of glitches by combining human intuition and pattern recognition with the power of computers to process large amounts of data systematically
- Zooniverse Project volunteers will morphologically classify glitches from the LIGO data stream, which are used to train machine learning algorithms for further classification
- In addition to the characterization and elimination of problematic noise in the aLIGO data stream, Gravity Spy promotes gravitational wave science and involves the lay public in scientific progress
Vicky Kalogera, Northwestern University
Kevin Crowston, Syracuse University
Shane Larson, Northwestern University, Adler Planetarium
Josh Smith, California State University - Fullerton
Laura Trouille, Adler Planetarium, Zooniverse
California State University - Fullerton
University of Alabama at Huntsville
Center for Space Plasma and Aeronomic Research
Media Round Up
- Read the Daily Zooniverse Announcement.
- Read Citizen Scientists Join Search for Gravitational Waves on Symmetry.
- Read Researchers Turn to “Citizen Scientists” for Help Identifying Gravitational Waves from the University of Alabama, Huntsville.
- Read about Gravity Spy in Syracuse University's The Daily Orange.
- Read Physics Student a 'Gravity Spy' from California State University, Fullerton.
- Read LIGO Magazine article Written by graduate student, Michael Zevin.
- Read Advancing the Search for Gravitational Waves with Next-Generation Citizen Science on CQG+.