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We investigate the impact of transient noise artifacts, or {\it glitches}, ongravitational wave inference, and the efficacy of data cleaning procedures inrecovering unbiased source properties. Due to their time-frequency morphology,broadband glitches demonstrate moderate to significant biasing of posteriordistributions away from true values. In contrast, narrowband glitches havenegligible biasing effects owing to distinct signal and glitch morphologies. Weinject simulated binary black hole signals into data containing three commonglitch types from past LIGO-Virgo observing runs, and reconstruct both signaland glitch waveforms using {\tt BayesWave}, a wavelet-based Bayesian analysis.We apply the standard LIGO-Virgo-KAGRA deglitching procedure to the detectordata - we subtract the glitch waveform estimated by the joint {\tt BayesWave}inference before performing parameter estimation with detailed compact binarywaveform models. We find that this deglitching effectively mitigates bias frombroadband glitches, with posterior peaks aligning with true values postdeglitching. This provides a baseline validation of existing techniques, whiledemonstrating waveform reconstruction improvements to the Bayesian algorithmfor robust astrophysical characterization in glitch-prone detector data.

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