@Article{CiCP-25-963,
author = {Marco Cavaglià, Kai Staats and Teerth Gill},
title = {Finding the Origin of Noise Transients in LIGO Data with Machine Learning},
journal = {Communications in Computational Physics},
year = {2018},
volume = {25},
number = {4},
pages = {963--987},
abstract = {<p style="text-align: justify;">Quality improvement of interferometric data collected by gravitational-wave 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.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2018-0092},
url = {http://global-sci.org/intro/article_detail/cicp/12886.html}
}