Characterizing the efficacy of methods to subtract terrestrial transient noise near gravitational wave events and the effects on parameter estimation

We investigate the impact of transient noise artifacts, or {\it glitches}, on gravitational wave inference, and the efficacy of data cleaning procedures in recovering unbiased source properties. Due to their time-frequency morphology, broadband glitches demonstrate moderate to significant biasing of posterior distributions away from true values. In contrast, narrowband glitches have negligible biasing effects owing to distinct signal and glitch morphologies. We inject simulated binary black hole signals into data containing three common glitch types from past LIGO-Virgo observing runs, and reconstruct both signal and glitch waveforms using {\tt BayesWave}, a wavelet-based Bayesian analysis. We apply the standard LIGO-Virgo-KAGRA deglitching procedure to the detector data - we subtract the glitch waveform estimated by the joint {\tt BayesWave} inference before performing parameter estimation with detailed compact binary waveform models. We find that this deglitching effectively mitigates bias from broadband glitches, with posterior peaks aligning with true values post deglitching. This 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.