Detecting stochastic gravitational wave background from cosmic strings with next-generation detector networks: Component separation based on a multi-source astrophysical foreground noise model

Abstract

Detecting stochastic gravitational wave background (SGWB) from cosmic strings is crucial for unveiling the evolutionary laws of the early universe and validating non-standard cosmological models. This study presents the first systematic evaluation of the detection capabilities of next-generation ground-based gravitational wave detector networks for cosmic strings. By constructing a hybrid signal model incorporating multi-source astrophysical foreground noise, including compact binary coalescences (CBCs) and compact binary hyperbolic encounters (CBHEs), we propose an innovative parameter estimation methodology based on multi-component signal separation. Numerical simulations using one-year observational data reveal three key findings: (1) The CE4020ET network, comprising the Einstein Telescope (ET-10 km) and the Cosmic Explorer (CE-40 km and CE-20 km), achieves nearly one order of magnitude improvement in constraining the cosmic string tension Gμ compared to individual detectors, reaching a relative uncertainty Gμ / Gμ < 0.5 for Gμ > 3.5 × 10-15 under standard cosmological framework; (2) The network demonstrates enhanced parameter resolution in non-standard cosmological scenarios, providing a novel approach to probe pre-Big Bang Nucleosynthesis cosmic evolution; (3) Enhanced detector sensitivity amplifies CBHE foreground interference in parameter estimation, while precise modeling of such signals could further refine Gμ constraints by 1-2 orders of magnitude. This research not only quantifies the detection potential of third-generation detector networks for cosmic string models but also elucidates the intrinsic connection between foreground modeling precision and cosmological parameter estimation accuracy, offering theoretical foundations for optimizing scientific objectives of next-generation gravitational wave observatories.

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