Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning

Understand

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy.

• A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources.
• The citizen-science project Gravity Spy has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning.
• We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization.