Bayesian parameter estimation of massive black hole binaries with TianQin and LISA

This paper thoroughly analyzes the impact of various parameter changes on the estimation of parameters for massive black hole binaries (MBHBs) systems using a Bayesian inference technique with the upcoming TianQin and LISA gravitational wave detectors. Several designed MBHB systems were chosen to compare with a fiducial system and explore the influence of parameters such as sky localization, inclination angle, antialigned spin, large mass ratio, and low mass. Additionally, the two known MBHB candidates OJ287 and Tick-Tock are also considered in our study. Utilizing Bayesian inference, we explored the 11-dimensional parameter space and found that parameter relationships depend significantly on the source's injected values and waveform phases. Our results indicate that while signal-to-noise ratio (SNR) is important, it does not solely determine parameter estimation accuracy. Joint detection by TianQin and LISA was particularly effective in breaking degeneracies and improving the precision of extrinsic parameters by approximately an order of magnitude. We also found about three orders of improvement for sky localization, partly due to the time delay between the two detectors. However, the primary contributor is the different configurations of the detectors that can help break down the parameter degeneracies. Moreover, when the source is near the surface of TianQin's orbital plane, the Doppler effects from TianQin's movement around the Earth play a significant role in determining sky localization. The study also demonstrates that our methodology is robust to noise, with most parameter estimates remaining within a $2\ensuremath{\sigma}$ confidence region even in the presence of noise.