Waveform accuracy and systematic uncertainties in current gravitational wave observations

TL;DR

This study investigates how inaccuracies in post-Newtonian waveform models cause systematic errors in gravitational wave parameter estimation and proposes a method to mitigate these errors by including higher-order PN coefficients as parameters.

Contribution

It demonstrates the impact of higher-order post-Newtonian corrections on systematic errors and introduces a marginalization method to reduce these errors in gravitational wave data analysis.

Findings

Higher-order PN corrections can cause significant systematic errors.

Marginalizing over PN coefficients reduces systematic errors.

Trade-off between systematic and statistical errors.

Abstract

The post-Newtonian formalism plays an integral role in the models used to extract information from gravitational wave data, but models that incorporate this formalism are inherently approximations. Disagreement between an approximate model and nature will produce mismodeling biases in the parameters inferred from data, introducing systematic error. We here carry out a proof-of-principle study of such systematic error by considering signals produced by quasi-circular, inspiraling black hole binaries through an injection and recovery campaign. In particular, we study how unknown, but calibrated, higher-order post-Newtonian corrections to the gravitational wave phase impact systematic error in recovered parameters. As a first study, we produce injected data of non-spinning binaries as detected by a current, second-generation network of ground-based observatories and recover them with models of varying PN order in the phase. We find that the truncation of higher order (>3.5) post-Newtonian corrections to the phase can produce significant systematic error even at signal-to-noise ratios of current detector networks. We propose a method to mitigate systematic error by marginalizing over our ignorance in the waveform through the inclusion of higher-order post-Newtonian coefficients as new model parameters. We show that this method can reduce systematic error greatly at the cost of increasing statistical error.