Isotropic stochastic gravitational wave background reconstruction for Taiji constellation

TL;DR

This paper develops a data analysis pipeline for the Taiji space mission to detect and reconstruct the stochastic gravitational wave background, addressing challenges of noise separation and unknown spectral shapes.

Contribution

It introduces a novel pipeline using trans-dimensional MCMC for reconstructing the SGWB with unknown spectral morphology in space-based interferometer data.

Findings

Successfully recovered injected background parameters in simulations.

Extended analysis to unknown spectral shapes using advanced MCMC methods.

Demonstrated the pipeline's potential for future space mission data analysis.

Abstract

The stochastic gravitational wave background is a broadband target from diverse astrophysical and cosmological sources. The background falls within the mHz frequency band could become a potential observable for future space-based interferometers. Taiji, a proposed space mission slated for launch in the 2030s, will enable the study of such a background. However, the unique characteristics of space missions pose distinctive challenges for separating the stochastic background from instrumental noise. To address the data analysis requirements, we develop a preliminary pipeline to search for the SGWB and evaluate its performance with Taiji simulation datasets. At present, we demonstrate that the algorithm can successfully recover the parameters of injected background with a known spectral density after setting aside the complication of galactic binaries foreground. Furthermore, by employing the trans-dimensional Markov Chain Monte Carlo method, we extend the analysis to reconstruct the background with unknown spectral morphology.