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In this paper we investigate the impact of transient noise artifacts, or 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 BayesWave, a wavelet-based Bayesian analysis. We apply the standard LIGO-Virgo-KAGRA deglitching pro- cedure to the detector data, which consists of subtracting from calibrated LIGO data the glitch waveform estimated by the joint BayesWave 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. 

The post-Newtonian formalism plays an integral role in the models used to extract information from gravitational wave data, but models that incorporate this formalism are inherently approximations. Disagreement between an approximate model and nature will produce mismodeling biases in the parameters inferred from data, introducing systematic error. We here carry out a proof-of- principle study of such systematic error by considering signals produced by quasi-circular, inspiraling black hole binaries through an injection and recovery campaign. In particular, we study how un- known, but calibrated, higher-order post-Newtonian corrections to the gravitational wave phase impact systematic error in recovered parameters. As a first study, we produce injected data of non-spinning binaries as detected by a current, second-generation network of ground-based observatories and recover them with models of varying PN order in the phase. We find that the truncation of higher order (>3.5) post-Newtonian corrections to the phase can produce significant systematic error even at signal-to-noise ratios of current detector networks. We propose a method to mitigate systematic error by marginalizing over our ignorance in the waveform through the inclusion of higher-order post-Newtonian coefficients as new model parameters. We show that this method can reduce systematic error greatly at the cost of increasing statistical error.