Characterizing Signal Parameter Bias in the Presence of a Glitch

Data from gravitational-wave (GW) detectors often contains non-Gaussian transient noise, known as glitches. In the upcoming fourth observation run, sensitivity improvements are expected to increase the rate of GW detection, therefore increasing the likelihood glitches will coincide with astrophysical signals. This type of glitch-signal overlap has the potential to significantly bias parameter estimation of the GW event. Past glitch mitigation efforts have included subtracting a model of the glitch from the data or removing affected portions of the frequency-time space. However, both of these methods can take weeks to process. This would be particularly problematic if a low mass, potentially EM-bright event was confused for a high mass event. In this talk, I will discuss a novel approach for rapidly characterizing the parameter bias of a candidate event when detected in the presence of a well-known glitch. We quantify shifts in measured posterior distributions for compact binary coalescence (CBC) gravitational-wave signals interacting with glitches as a function of time between the signal merger time and the glitch. The results of this study will also provide preliminary suggestions to candidate event reviewers as to what constitutes a safe time separation between a GW signal and a glitch.