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

TL;DR

BayesLine is a Bayesian algorithm that models gravitational wave detector noise spectra using splines and Lorentzians, improving noise characterization for better detection and analysis of weak signals.

Contribution

It introduces a novel multi-component Bayesian method for spectral estimation that accounts for both broad-band and narrow-band noise features in gravitational wave data.

Findings

Demonstrated on LIGO data from recent science runs

Accurately models both broad-band noise and spectral lines

Enables marginalization over noise uncertainties in analysis

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.