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. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run, we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program.
Bibliographical noteFunding Information:
First, and foremost, we thank the many Gravity Spy participants that make this work possible. We thank Eliu Huerta, Alex Urban, and Patrick Sutton for their useful comments. Gravity Spy is partly supported by the National Science Foundation award INSPIRE 15-47880. O. P. is supported by NSF award AST-1559694. M. C. is supported by the David and Ellen Lee Postdoctoral Fellowship at the California Institute of Technology. C. P. .L. B. is supported by the CIERA Board of Visitors Research Professorship. In addition, computing was provided by the LIGO Data Grid which is supported by the National Science Foundation Grants PHY-0757058 and PHY-0823459. This work also used computing resources at CIERA funded by NSF PHY-1126812. This paper has been assigned LIGO document number LIGO-P1800352.
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