Correcting Compact Binary Inspiral Waveform Uncertainty in Bayesian Inference Parameter Estimation

Gravitational-wave models for the inspiral and merger of black holes and neutron stars have intrinsic uncertainties. These may arise from different approximations of general relativity used to generate the waveform, as well as from potential modifications to general relativity. Differences between models with the same astrophysical parameters can be quantified as corrections to the frequency domain amplitude, △Α(f), and phase, ΔΦ(f). In this work, we use spline-based phenomenological models to add variability to a baseline waveform model during parameter estimation runs. Prior distributions of the spline parameters are motivated by the expected faithfulness of different waveform models with each other. We demonstrate that injected waveform modifications can be recovered, and that adding waveform variability during parameter estimation runs can reduce systematic bias in recovered astrophysical parameters.