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

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.

22 pages, 17 figures

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• Systematic biases due to waveform mismodeling in parametrized post-Einsteinian tests of general relativity: The impact of neglecting spin precession and higher modes
• Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization
• No Glitch in the Matrix: Robust Reconstruction of Gravitational Wave Signals Under Noise Artifacts
• Null Stream Based Third-generation-ready Glitch Mitigation for Gravitational Wave Measurements
• Inferring the spins of merging black holes in the presence of data-quality issues
• Joint inference for gravitational wave signals and glitches using a data-informed glitch model
• Glitches far from transient gravitational-wave events do not bias inference
• Machine Learning Confirms GW231123 is a "Lite" Intermediate Mass Black Hole Merger
• Identification of Noise-Associated Glitches in KAGRA O3GK with Hveto
• MaxWave: Rapid maximum likelihood wavelet reconstruction of non-Gaussian features in gravitational wave data