Volume 25, Issue 4
Finding the Origin of Noise Transients in LIGO Data with Machine Learning

Marco Cavaglià, Kai Staats & Teerth Gill

Commun. Comput. Phys., 25 (2019), pp. 963-987.

Published online: 2018-12

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  • Abstract

Quality improvement of interferometric data collected by gravitationalwave detectors such as Advanced LIGO and Virgo is mission critical for the success of gravitational-wave astrophysics. Gravitational-wave detectors are sensitive to a variety of disturbances of non-astrophysical origin with characteristic frequencies in the instrument band of sensitivity. Removing non-astrophysical artifacts that corrupt the data stream is crucial for increasing the number and statistical significance of gravitational-wave detections and enabling refined astrophysical interpretations of the data. Machine learning has proved to be a powerful tool for analysis of massive quantities of complex data in astronomy and related fields of study. We present two machine learning methods, based on random forest and genetic programming algorithms, that can be used to determine the origin of non-astrophysical transients in the LIGO detectors. We use two classes of transients with known instrumental origin that were identified during the first observing run of Advanced LIGO to show that the algorithms can successfully identify the origin of non-astrophysical transients in real interferometric data and thus assist in the mitigation of instrumental and environmental disturbances in gravitational-wave searches. While the datasets described in this paper are specific to LIGO, and the exact procedures employed were unique to the same, the random forest and genetic programming code bases and means by which they were applied as a dual machine learning approach are completely portable to any number of instruments in which noise is believed to be generated through mechanical couplings, the source of which is not yet discovered.

  • Keywords

Machine learning gravitational waves noise mitigation.

  • AMS Subject Headings

68T05 83-04 83C35 68T20

  • Copyright

COPYRIGHT: © Global Science Press

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