Speaker
Description
In the era of third-generation gravitational wave detectors such as the Einstein Telescope and Cosmic Explorer, there are multiple challenges for data analysis. One of the most important problems in parameter estimation is accounting for waveform systematics. The waveform models may struggle to meet the accuracy requirements of these detectors across the full range of parameter space. This challenge may stem from a limited number of numerical relativity simulations or the omission of crucial physical effects in the waveform models. Using inaccurate waveform models will introduce biases in the recovery of source parameters and may negatively affect downstream analyses, such as tests of general relativity, the estimation of the Hubble constant, and the inference of population properties. We propose a generic framework that quantifies uncertainties in waveform models by parametrizing errors in their amplitude and phase. This framework can correct biases in parameter estimation and recover the functional form of the deviation in the model for high-SNR signals. This data-driven approach could also provide valuable feedback to waveform model developers and numerical relativity simulations, particularly for 'golden binaries.' We tested this approach through a simulated injection and recovery campaign. This approach can mitigate systematic errors due to waveform inaccuracies even when we know little about them, and it can also mitigate errors caused by other data-analysis artifacts, such as glitches near the signal.