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arxiv: 1410.3852 · v1 · submitted 2014-10-14 · 🌀 gr-qc · astro-ph.HE· astro-ph.IM

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BayesLine: Bayesian Inference for Spectral Estimation of Gravitational Wave Detector Noise

Tyson B. Littenberg , Neil J. Cornish
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classification 🌀 gr-qc astro-ph.HEastro-ph.IM
keywords noisedatabayeslineestimationgravitationalspectralwavewill
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Gravitational wave data from ground-based detectors is dominated by instrument noise. Signals will be comparatively weak, and our understanding of the noise will influence detection confidence and signal characterization. Mis-modeled noise can produce large systematic biases in both model selection and parameter estimation. Here we introduce a multi-component, variable dimension, parameterized model to describe the Gaussian-noise power spectrum for data from ground-based gravitational wave interferometers. Called BayesLine, the algorithm models the noise power spectral density using cubic splines for smoothly varying broad-band noise and Lorentzians for narrow-band line features in the spectrum. We describe the algorithm and demonstrate its performance on data from the fifth and sixth LIGO science runs. Once fully integrated into LIGO/Virgo data analysis software, BayesLine will produce accurate spectral estimation and provide a means for marginalizing inferences drawn from the data over all plausible noise spectra.

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