Speaker
Description
Parameter estimation (PE) for gravitational wave merger events relies on the accuracy of the waveform models and the noise model. The PE result might suffer from systematic errors if any of the above assumptions break down. Waveform systematics may arise due to mismodelling in the waveform models or numerical relativity simulations. The noise model may break down in the presence of glitches or non-stationarity near the signal. As the sensitivity of the detector network improves, it is essential to incorporate systematic error mitigation schemes into the PE data analysis pipelines.
In this work, we present a framework to account for waveform systematic errors by introducing parametrizations that compensate for amplitude and phase errors in the waveform models. The expected error budget in a reference waveform model is used as a prior distribution in PE. However, in the absence of the knowledge of these error budgets, we can still use conservative priors on amplitude and phase errors. We test our framework with simulated signals with and without systematic errors in the waveform model. We demonstrate that our framework can account for waveform systematic errors and also address biases resulting from the omission of physical effects in the waveform model description. We will also show the PE results for the GW events from publicly available data.
We will also discuss the degeneracy of the waveform systematics with those due to detector calibration. Furthermore, we will discuss the feasibility of a comprehensive data analysis pipeline that includes systematic errors mitigation due to waveform models uncertainties and noise artifacts.
