Issues of mismodeling gravitational-wave data for parameter estimation

Bayesian inference is used to extract unknown parameters from gravitational-wave signals. Detector noise is typically modeled as stationary, although data from the LIGO and Virgo detectors is not stationary. We demonstrate that the posterior of estimated waveform parameters is no longer valid under the assumption of stationarity. We show that while the posterior is unbiased, the errors will be under- or overestimated compared to the true posterior. A formalism was developed to measure the effect of the mismodeling, and found the effect of any form of nonstationarity has an effect on the results, but are not significant in certain circumstances. We demonstrate the effect of short-duration Gaussian noise bursts and persistent oscillatory modulation of the noise on binary-black-hole-like signals. In the case of short signals, nonstationarity in the data does not have a large effect on the parameter estimation, but the errors from nonstationary data containing signals lasting tens of seconds or longer will be several times worse than if the noise was stationary. Accounting for this limiting factor in parameter sensitivity could be very important for achieving accurate astronomical results. This methodology for handling the nonstationarity will also be invaluable for analysis of waveforms that last minutes or longer, such as those we expect to see with the Einstein Telescope.

Automated summary

This study focuses on using Bayesian inference to extract unknown parameters from gravitational wave signals. The research finds that the posterior of estimated waveform parameters is no longer valid under the assumption of stationary noise, leading to under- or overestimated errors compared to the true posterior. While nonstationarity in short signals has minimal impact on parameter estimation, nonstationary data containing signals lasting tens of seconds or longer will result in significantly worse errors than stationary noise.