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

Zevin, M., Coughlin, S., Bahaadini, S., Besler, E., Rohani, N., Allen, S., et al. (2017). Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science. Classical and Quantum Gravity, 34(6): 064003. doi:10.1088/1361-6382/aa5cea.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-39F3-E Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-AA2F-9
Genre: Journal Article

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 Creators:
Zevin, Michael, Author
Coughlin, Scott, Author
Bahaadini, Sara, Author
Besler, Emre, Author
Rohani, Neda, Author
Allen, Sarah, Author
Cabero, Miriam1, Author
Crowston, Kevin, Author
Katsaggelos, Aggelos, Author
Larson, Shane, Author
Lee, Tae Kyoung, Author
Lintott, Chris, Author
Littenberg, Tyson, Author
Lundgren, Andrew2, Author              
Oesterlund, Carsten, Author
Smith, Joshua, Author
Trouille, Laura, Author
Kalogera, Vicky, Author
Affiliations:
1AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, Hannover, DE, escidoc:24009              
2Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, escidoc:24011              

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Free keywords: General Relativity and Quantum Cosmology, gr-qc, Astrophysics, High Energy Astrophysical Phenomena, astro-ph.HE, Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM, Physics, Instrumentation and Detectors, physics.ins-det
 Abstract: (abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.

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 Dates: 2016-11-142017
 Publication Status: Published in print
 Pages: 27 pages, 11 figures, submitted to CQG
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: arXiv: 1611.04596
URI: http://arxiv.org/abs/1611.04596
DOI: 10.1088/1361-6382/aa5cea
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Title: Classical and Quantum Gravity
Source Genre: Journal
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Pages: - Volume / Issue: 34 (6) Sequence Number: 064003 Start / End Page: - Identifier: -