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The evidence of cosmic acceleration and observational constraints

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arxiv 1912.07375 v2 pith:ZHSCABDU submitted 2019-12-16 astro-ph.CO gr-qchep-th

classification astro-ph.COgr-qchep-th
keywords datacosmicexpansionaccelerationratechronometerscompilationevidence
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Directly comparing the 6 expansion rate measured by type Ia supernovae data and the lower bound on the expansion rate set by the strong energy conditions or the null hypothesis that there never exists cosmic acceleration, we see $3\sigma$ direct evidence of cosmic acceleration and the $Rh=ct$ model is strongly excluded by the type Ia supernovae data. We also use Gaussian process method to reconstruct the expansion rate and the deceleration parameter from the 31 cosmic chronometers data and the 6 data points on the expansion rate measured from type Ia supernoave data, the direct evidence of cosmic acceleration is more than $3\sigma$ and we find that the transition redshift $z_t=0.60_{-0.12}^{+0.21}$ at which the expansion of the Universe underwent the transition from acceleration to deceleration. The Hubble constant inferred from the cosmic chronometers data with the Gaussian process method is $H_0=67.46\pm4.75$ Km/s/Mpc. To understand the properties of cosmic acceleration and dark energy, we fit two different two-parameter models to the observational data, and we find that the constraints on the model parameters from either the full distance modulus data by the Pantheon compilation or the compressed expansion rate data are very similar, and the derived Hubble constants are consistent with the Planck 2018 result. Our results confirm that the 6 compressed expansion rate data can replace the full 1048 distance modulus data from the Pantheon compilation. We derive the transition redshift $z_t=0.61_{-0.16}^{+0.24}$ by fitting a simple $q(z)$ model to the combination of cosmic chronometers data and the Pantheon compilation, the result is consistent with that obtained from the reconstruction with Gaussian process. Abridged.

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