Joint inference for gravitational wave signals and glitches using a data-informed glitch model

TL;DR

This paper presents a machine learning approach using normalising flows to model and jointly infer gravitational wave signals and glitches, improving parameter estimation by reducing bias caused by non-Gaussian noise transients.

Contribution

It introduces a data-informed, parameterised glitch model integrated into Bayesian inference, enhancing gravitational wave data analysis.

Findings

Effective glitch removal from real data

Significant bias reduction in source parameters

Improved Bayesian model selection accuracy

Abstract

Gravitational wave data are often contaminated by non-Gaussian noise transients, glitches, which can bias the inference of astrophysical signal parameters. Traditional approaches either subtract glitches in a pre-processing step, or a glitch model can be included from an agnostic wavelet basis (e.g. BayesWave). In this work, we introduce a machine-learning-based approach to build a parameterised model of glitches. We train a normalising flow on known glitches from the Gravity Spy catalogue, constructing an informative prior on the glitch model. By incorporating this model into the Bayesian inference analysis with Bilby, we estimate glitch and signal parameters simultaneously. We demonstrate the performance of our method through bias reduction, glitch identification and Bayesian model selection on real glitches. Our results show that this approach effectively removes glitches from the data, significantly improving source parameter estimation and reducing bias.