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

Mixed citation behavior. Most common role is method (62%).

13 Pith papers citing it
Method 62% of classified citations
abstract

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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Tests of General Relativity with GWTC-3

gr-qc · 2021-12-13 · accept · novelty 3.0

No evidence for physics beyond general relativity is found in the analysis of 15 GW events from GWTC-3, with consistency in residuals, PN parameters, and remnant properties.

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