Separating Gravitational Wave Signals from Instrument Artifacts

TL;DR

This paper introduces a Bayesian method that models non-stationary noise and glitches in gravitational wave data, enabling detection of weaker signals than traditional techniques.

Contribution

The paper presents a novel Bayesian approach with wavelet-based glitch modeling and trans-dimensional MCMC for improved gravitational wave detection amidst noise artifacts.

Findings

Enhanced detection sensitivity for low amplitude signals

Effective modeling of non-stationary, non-Gaussian noise and glitches

Demonstrated success on simulated data with realistic noise and signals

Abstract

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 filtering 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 detection 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 comprised 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.