Uploaded July 2022 | Updated September 2026, 2 hours ago
Extracting gravitational wave signals from individual Galactic binaries (GBs) against their self-generated confusion noise is a key data analysis challenge for space-borne detectors in the ∼ 0.1 mHz to ∼ 10 mHz band. Given the likely prospect that there will be multiple such detectors, namely LISA, Taiji, and Tianqin, with overlapping operational periods in the next decade, it is important to examine the extent to which the joint analysis of their data can benefit GB resolution and parameter estimation. To investigate this, we use realistic simulated LISA and Taiji data containing the set of 30 × 106 GBs used in the first LISA Data Challenge (Radler), and an iterative source extraction method called GBSIEVER introduced in an earlier work. We find that a coherent network analysis of LISA-Taiji data boosts the number of confirmed sources by ≈ 75% over that from a single detector. The residual after subtracting out the reported sources from the data of any given detector is much closer to the confusion noise expected from an ideal, but infeasible, multisource resolution method that perfectly removes all sources above a given signal-to-noise ratio threshold.
Authors: Xuehao Zhang, Shaodong Zhao, Soumya Mohanty, Xiaobo Zou, Yuxiao Liu
Presenter: Xuehao Zhang
Extracting gravitational wave signals from individual Galactic binaries (GBs) against their self-generated confusion noise is a key data analysis challenge for space-borne detectors in the ∼ 0.1 mHz to ∼ 10 mHz band. Given the likely prospect that there will be multiple such detectors, namely LISA, Taiji, and Tianqin, with overlapping operational periods in the next decade, it is important to examine the extent to which the joint analysis of their data can benefit GB resolution and parameter estimation. To investigate this, we use realistic simulated LISA and Taiji data containing the set of 30 × 106 GBs used in the first LISA Data Challenge (Radler), and an iterative source extraction method called GBSIEVER introduced in an earlier work. We find that a coherent network analysis of LISA-Taiji data boosts the number of confirmed sources by ≈ 75% over that from a single detector. The residual after subtracting out the reported sources from the data of any given detector is much closer to the confusion noise expected from an ideal, but infeasible, multisource resolution method that perfectly removes all sources above a given signal-to-noise ratio threshold.
Authors: Xuehao Zhang, Shaodong Zhao, Soumya Mohanty, Xiaobo Zou, Yuxiao Liu
Presenter: Xuehao Zhang
