Signal identification of the stochastic gravitational wave background in the Mock LISA Data Challenge

Detecting the stochastic gravitational wave background, revealing the evolutionary processes of the very early universe along with the characteristics of astrophysical source populations is among the leading scientific objectives of the space-borne gravitational wave detector, which will significantly advance our understanding in fundamental physics and astrophysics. In this work, we introduce the null channel method for detecting the stochastic gravitational wave background within a single LISA-like space gravitational wave detector, as well as model and analyze the instrument noise in detail. Based on this, we put the Mock LISA Data Challenge 3.5 training dataset into practice. In the Bayesian data analysis framework, Markov chain Monte Carlo algorithm is used to directly evaluate the physical parameters of the injected signal, and the instrument noise and stochastic gravitational wave background are accurately reconstructed.