Parameter estimation for Einstein-dilaton-Gauss-Bonnet gravity with ringdown signals

TL;DR

This paper assesses how future space-based gravitational wave detectors can constrain Einstein-dilaton-Gauss-Bonnet gravity parameters using ringdown signals from black hole mergers, employing Fisher analysis and Bayesian inference.

Contribution

It introduces a comprehensive analysis of the parameter estimation capabilities of LISA, TaiJi, and TianQin for EdGB gravity using ringdown signals, including Fisher and Bayesian methods.

Findings

LISA and TaiJi better constrain massive black hole parameters.

TianQin more effective for less massive black holes.

Constraint accuracy decreases as the deviation parameter approaches zero.

Abstract

Future space-based gravitational-wave detectors will detect gravitational waves with high sensitivity in the millihertz frequency band, which provides more opportunities to test theories of gravity than ground-based ones. The study of quasinormal modes (QNMs) and their application to testing gravity theories have been an important aspect in the field of gravitational physics. In this study, we investigate the capability of future space-based gravitational wave detectors such as LISA, TaiJi, and TianQin to constrain the dimensionless deviating parameter for Einstein-dilaton-Gauss-Bonnet (EdGB) gravity with ringdown signals from the merger of binary black holes. The ringdown signal is modeled by the two strongest QNMs in EdGB gravity. Taking into account time-delay interferometry, we calculate the signal-to-noise ratio (SNR) of different space-based detectors for ringdown signals to analyze their capabilities. The Fisher information matrix is employed to analyze the accuracy of parameter estimation, with particular focus on the dimensionless deviating parameter for EdGB gravity. The impact of the parameters of gravitational wave sources on the estimation accuracy of the dimensionless deviating parameter has also been studied. We find that the constraint ability of EdGB gravity is limited because the uncertainty of the dimensionless deviating parameter increases with the decrease of the dimensionless deviating parameter. LISA and TaiJi has more advantages to constrain the dimensionless deviating parameter to a more accurate level for the massive black hole, while TianQin is more suitable for less massive black holes. Bayesian inference method is used to perform parameter estimation on simulated data, which verifies the reliability of the conclusion.