Addressing Systematic Error in Binary Black Hole Parameter Estimation through Improved Waveform Models

Accurate parameter estimation of coalescing binary black hole, gravitational-wave signals can be hindered by systematic error introduced by waveform mismodeling, potentially leading to biased inferences. In this talk, I will discuss a method to mitigate these systematic bias within the inspiral-merger-ringdown (IMR) phenomenological waveform family by employing an enhanced IMRPhenomD model. The method involves accounting for and marginalizing over fitting coefficients in the enhanced waveform family that capture mismodeling error. Probability distributions for the model fitting coefficients are generated through training against a dataset of highly accurate, spinning surrogate waveforms. These distributions are then integrated as priors, allowing simultaneous sampling of astrophysical parameters and fitting coefficients during parameter estimation for known gravitational-wave events. Embracing the variability of phenomenological coefficients, this method aims to provide more reliable and unbiased astrophysical parameter estimates.