LIGO Document P1000084-v1

Separating Gravitational Wave Signals from Instrument Artifacts

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Central to the gravitational wave detection problem is the challenge of separating features in the
data produced by astrophysical sources from features produced by the detector. Matched filter-
ing provides an optimal solution for Gaussian noise, but in practice, transient noise excursions or
“glitches” complicate the analysis. Detector diagnostics and coincidence tests can be used to veto
many glitches which may otherwise be misinterpreted as gravitational wave signals. The glitches
that remain can lead to long tails in the matched filter search statistics and drive up the detec-
tion threshold. Here we describe a Bayesian approach that incorporates a more realistic model for
the instrument noise allowing for fluctuating noise levels that vary independently across frequency
bands, and deterministic “glitch fitting” using wavelets as “glitch templates”, the number of which
is determined by a trans-dimensional Markov chain Monte Carlo algorithm. We demonstrate the
method’s effectiveness on simulated data containing low amplitude gravitational wave signals from
inspiraling binary black hole systems, and simulated non-stationary and non-Gaussian noise com-
prised of a Gaussian component with the standard LIGO/Virgo spectrum, and injected glitches of
various amplitude, prevalence, and variety. Glitch fitting allows us to detect significantly weaker
signals than standard techniques.
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