2014/10/14 by Tyson B. Littenberg, Neil J. Cornish · 2 citations
Physics and Astronomy · #gr-qc #astro-ph.HE #astro-ph.IM
paper · pdf · doi:10.1103/physrevd.91.084034
published as Phys. Rev. D 91, 084034 (2015) · 17 pages, 10 figures
arxiv created 2014/10/14 · arxiv updated 2015/04/22
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.