Parameter estimation of stellar mass binary black holes in the network of TianQin and LISA
Xiangyu Lyu, En-Kun Li, Yi-Ming Hu
Abstract
We present a Bayesian parameter estimation progress to infer the stellar mass binary black hole properties by TianQin, LISA, and $\mathrm{TianQin}+\mathrm{LISA}$. 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 $\mathrm{TianQin}+\mathrm{LISA}$ could marginally increase the parameter estimation precision and narrow the $1\ensuremath{\sigma}$ 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.