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

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arxiv 2307.12244 v2 pith:M3GZBFGV submitted 2023-07-23 gr-qc astro-ph.HE

Xiangyu Lyu , En-Kun Li , Yi-Ming Hu This is my paper · ORCID
classification gr-qcastro-ph.HE
keywords tianqinlisaestimationmassparameterbinaryblackstellar
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Constraining Dipole Radiation with Multiband Gravitational Waves from Eccentric Binary Black Holes

    gr-qc 2026-04 unverdicted novelty 7.0 of 10

    Multiband observations of eccentric binary black holes can constrain dipole-radiation deviations from general relativity to |b| ≲ 10^{-7} for a GW231123-like event when combining one year of space-based data with grou...

  2. Constructing a gravitational wave analysis pipeline for extremely large mass ratio inspirals

    astro-ph.HE 2026-01 conditional novelty 6.0 of 10

    A hierarchical semi-coherent F-statistic plus particle-swarm pipeline recovers an injected Sgr A* XMRI from 90 days of simulated TianQin data with sub-percent parameter precision.

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