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A path to precision cosmology: Synergy between four promising late-universe cosmological probes

Authors :
Wu, Peng-Ju
Shao, Yue
Jin, Shang-Jie
Zhang, Xin
Wu, Peng-Ju
Shao, Yue
Jin, Shang-Jie
Zhang, Xin
Publication Year :
2022

Abstract

In the next decades, it is necessary to forge new late-universe cosmological probes to precisely constrain the Hubble constant and the equation of state of dark energy simultaneously. In this work, we show that the four typical late-universe cosmological probes, the 21 cm intensity mapping (IM), fast radio burst (FRB), gravitational wave (GW) standard siren, and strong gravitational lensing (SGL), are expected to be forged into useful tools in solving the Hubble tension and exploring dark energy. We propose that the synergy of them is rather important in cosmology. We simulate the 21 cm IM, FRB, GW, and SGL data based on the hypothetical observations of the Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX), the Square Kilometre Array (SKA), the Einstein Telescope (ET), and the Large Synoptic Survey Telescope (LSST), respectively. We find that the four probes show obviously different parameter degeneracy orientations in cosmological constraints, so any combination of them can break the parameter degeneracies and thus significantly improve the constraint precision. The joint 21 cm IM+FRB+GW+SGL data can provide the constraint errors of $\sigma(\Omega_{\rm m})=0.0022$ and $\sigma(H_0)=0.16\ \rm km\ s^{-1}\ Mpc^{-1}$ in the $\Lambda$CDM model, which meet the standard of precision cosmology, i.e., the constraint precision of parameters is better than 1%. In addition, the joint data give $\sigma(w)=0.020$ in the $w$CDM model, and $\sigma(w_0)=0.066$ and $\sigma(w_a)=0.25$ in the $w_0w_a$CDM model, which are all better than the constraints obtained by the CMB+BAO+SN data. We show that the synergy between the four late-universe cosmological probes has magnificent prospects.<br />Comment: 13 pages, 7 figures

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1336691677
Document Type :
Electronic Resource