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Home » INSPIRE: Teaming Citizen Science with Machine Learning to Deepen LIGO's View of the Cosmos

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Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

Publication Type:

Journal Article

Authors:

Michael Zevin; Scott Coughlin; Sara Bahaadini; Emre Besler; Neda Rohani; Sarah Allen; Miriam Cabero; Kevin Crowston; Aggelos Katsaggelos; Shane Larson; Tae Kyoung Lee; Chris Lintott; Tyson Littenberg; Andrew Lundgren; Carsten Oesterlund; Joshua Smith; Laura Trouille; Vicky Kalogera

Source:

Classical and Quantum Gravity, Volume 34, p.064003 (2017)
‹ INSPIRE: Teaming Citizen Science with Machine Learning to Deepen LIGO's View of the Cosmos up Teaching Citizen Scientists to Categorize Glitches using Machine-Learning-Guided Training ›
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