Kavli Affiliate: Erotokritos Katsavounidis
| First 5 Authors: Rahul Biswas, Lindy Blackburn, Junwei Cao, Reed Essick, Kari Alison Hodge
| Summary:
The sensitivity of searches for astrophysical transients in data from the
LIGO is generally limited by the presence of transient, non-Gaussian noise
artifacts, which occur at a high-enough rate such that accidental coincidence
across multiple detectors is non-negligible. Furthermore, non-Gaussian noise
artifacts typically dominate over the background contributed from stationary
noise. These "glitches" can easily be confused for transient gravitational-wave
signals, and their robust identification and removal will help any search for
astrophysical gravitational-waves. We apply Machine Learning Algorithms (MLAs)
to the problem, using data from auxiliary channels within the LIGO detectors
that monitor degrees of freedom unaffected by astrophysical signals. The number
of auxiliary-channel parameters describing these disturbances may also be
extremely large; an area where MLAs are particularly well-suited. We
demonstrate the feasibility and applicability of three very different MLAs:
Artificial Neural Networks, Support Vector Machines, and Random Forests. These
classifiers identify and remove a substantial fraction of the glitches present
in two very different data sets: four weeks of LIGO’s fourth science run and
one week of LIGO’s sixth science run. We observe that all three algorithms
agree on which events are glitches to within 10% for the sixth science run
data, and support this by showing that the different optimization criteria used
by each classifier generate the same decision surface, based on a
likelihood-ratio statistic. Furthermore, we find that all classifiers obtain
similar limiting performance, suggesting that most of the useful information
currently contained in the auxiliary channel parameters we extract is already
being used.
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