Assessing and Mitigating the Impact of Glitches on Gravitational-Wave Parameter Estimation: a Model Agnostic Approach

TL;DR

This paper evaluates how transient noise glitches affect gravitational-wave parameter estimation and demonstrates that subtracting modeled glitches using a wavelet-based Bayesian method effectively reduces bias, improving astrophysical inference accuracy.

Contribution

It introduces a model-agnostic Bayesian approach for waveform reconstruction and glitch subtraction, validating its effectiveness in mitigating bias in gravitational-wave data analysis.

Findings

Subtracting glitches reduces bias in parameter estimation.

Broadband glitches cause significant bias, narrowband glitches do not.

Waveform reconstruction improves with glitch subtraction.

Abstract

In this paper we investigate the impact of transient noise artifacts, or {\it glitches}, on gravitational-wave inference from ground-based interferometer data, and test how modeling and subtracting these glitches affects the inferred parameters. Due to their time-frequency morphology, broadband glitches cause moderate to significant biasing of posterior distributions away from true values. In contrast, narrowband glitches induce negligible biasing effects, due to distinct signal and glitch morphologies. We inject simulated binary black hole signals into data containing three occurring glitch types from past LIGO-Virgo observing runs, and reconstruct both signal and glitch waveforms using \bw{}, a wavelet-based Bayesian analysis. We apply the standard LIGO-Virgo-KAGRA deglitching procedure to the detector data, which consists of subtracting from calibrated LIGO data the glitch waveform estimated by the joint \bw{} inference. {We produce posterior distributions on the parameters of the injected signal before and after subtracting the glitch,} and we {show that removing the transient noise} effectively mitigates bias from broadband glitches. This study provides a baseline validation of existing techniques, while demonstrating waveform reconstruction improvements to the Bayesian algorithm for robust astrophysical characterization in glitch-prone detector data.