Parameter Estimation of Stellar Mass Binary Black Holes under the Network of TianQin and LISA

TL;DR

This paper evaluates the capability of TianQin, LISA, and their combination to estimate parameters of stellar mass binary black holes, highlighting the importance of spin effects and the benefits of joint observations.

Contribution

It introduces a Bayesian parameter estimation framework using full frequency response for TianQin and LISA, demonstrating improved precision with combined data and emphasizing spin considerations.

Findings

TianQin can effectively infer binary black hole parameters.

Joint TianQin+LISA observations marginally improve estimation accuracy.

Considering spin effects significantly impacts parameter estimation accuracy.

Abstract

We present a Bayesian parameter estimation progress to infer the stellar mass binary black hole properties by TianQin, LISA, and TianQin+LISA.Two typical stellar mass black hole binary systems, GW150914 and GW190521 are chosen as the fiducial sources. In this work, we establish the ability of TianQin to infer the parameters of those systems and first apply the full frequency response in TianQin's data analysis. We obtain the parameter estimation results and explain the correlation between them. We also find the TianQin+LISA could marginally increase the parameter estimation precision and narrow the $1\sigma$ area compared with TianQin and LISA individual observations. We finally demonstrate the importance of considering the effect of spin when the binaries have a nonzero component spin and great deviation will appear especially on mass, coalescence time and sky location.