Finding the origin of noise transients in LIGO data with machine learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Misc{Cavaglia:2018:arXiv,
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author = "Marco Cavaglia and Kai Staats and Teerth Gill",
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title = "Finding the origin of noise transients in {LIGO} data
with machine learning",
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howpublished = "arXiv",
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year = "2018",
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month = "13 " # dec,
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keywords = "genetic algorithms, genetic programming, Data
Analysis, Statistics and Probability",
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number = "LIGO-P1800043",
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URL = "https://arxiv.org/abs/1812.05225",
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size = "24 pages",
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abstract = "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 artefacts 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
data sets 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.",
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notes = "p20 'The future of ML applied to the analysis of
gravitational-wave detector data is certainly
bright.'
Dec 2018 gives Journal reference: Commun. Comput.
Phys., 25 (2019), pp. 963-987 DOI:
10.4208/cicp.OA-2018-0092 but doi not working
\cite{Cavaglia:2019:CCP}",
- }
Genetic Programming entries for
Marco Cavaglia
Kai Staats
Teerth Gill
Citations