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

TL;DR

The paper addresses detecting the SGWB from cosmic strings with next-generation ground-based detector networks by integrating a multi-source astrophysical foreground model (CBCs and CBHEs) and applying a component-separation method to estimate the string tension $G\mu$ under both standard and non-standard cosmologies. It builds a detailed forward model of CS spectra and foregrounds, then uses a Parida-based multi-component ML estimator across NG networks (CE4020, ET, CE4020ET) to recover $\Omega_{\rm CS}(f)$ and constrain $G\mu$ from simulated one-year data. The key contributions include (i) quantifying how foreground modeling—especially CBHE—impacts CS parameter recovery, (ii) demonstrating that the CE4020ET network significantly improves constraints (roughly an order of magnitude over single detectors) and (iii) showing sensitivity to non-standard pre-BBN cosmologies via features in the CS spectrum. This work provides a theoretical framework and practical guidance for optimizing third-generation GW observatories to probe early universe physics and cosmological evolution prior to BBN.

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 $ΔGμ/ Gμ< 0.5$ for $Gμ> 3.5 \times 10^{-15}$ 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.

Figures (9)

• Figure 1: Sensitivity of different detector networks in detecting the SGWB. Left: Amplitude spectral densities for the three detectors in the networks. Middle: Overlap reduction functions for each baseline in different detector networks, normalized such that $\gamma(f) = 1$ for co-located and co-aligned L-shaped detectors. Right: $1 \sigma$ PI curves showing the sensitivity of the various detector networks considered in this study to the stochastic gravitational-wave background after one year of observation Thrane:2013oya.
• Figure 2: GW fractional energy density spectrum in modified pre-BBN scenarios. This figure illustrates examples of the GW fractional energy density spectrum generated by CSs in modified pre-BBN scenarios with a reheating temperature of $T_{\rm rd} = 1 \rm GeV$. The shaded area represents the $1 \sigma$ PI sensitivity window of three networks Thrane:2013oya. The solid line represents the case with $G\mu = 1 \times 10^{-10}$, and the dashed line represents the case with $G\mu = 1 \times 10^{-16}$. The lines in different colors represent different values of $w$. For frequencies greater than 5 Hz, the spectrum can be approximated by a power-law function.
• Figure 3: GW fractional energy density spectrum from multiple sources. The shaded area represents the $1 \sigma$ PI sensitivity window of three networks Thrane:2013oya. The blue line depicts the GW generated by CSs ($G\mu = 1 \times 10^{-10}$, $w = 1/3$), the orange line depicts the GW from CBCs, the green line depicts the GW from CBHEs, and the black line represents the total energy spectrum of GWs generated by the three sources mentioned above.
• Figure 4: An example of simulated data generated by CE-40. The injected signal (orange) with $G\mu = 1 \times 10^{-10}$ and noise PSDs (green) are plotted together with the calculated PSD (blue) of a simulated data segment duration of 16 s.
• Figure 5: Marginalized distributions and confidence contours for GW component separation in CE4020ET network. For data case 1, 2, and 3 with $G\mu = 1 \times 10^{-13}$, $w = 1/3$ in the CE4020ET network, the one-dimensional marginalized distributions of individual components and the two-dimensional marginalized contours represent the 68.3% and 95.4% confidence levels, respectively. The green color represents the result of simulated data containing only astrophysical foreground noise. The blue color represents the component separation results with the full consideration of both CBC and CBHE foregrounds, while the orange color represents the results without considering the contribution of the CBHE foreground during component separation. The gray dashed line marks the injected true value. Using the component separation method, the amplitudes of multiple components are effectively estimated.