Decaying Dark Energy in Light of the Latest Cosmological Dataset
Department of Theoretical Physics and History of Science, University of the Basque Country UPV/EHU, Faculty of Science and Technology, Barrio Sarriena s/n, 48940 Leioa, Spain
Received: 2 August 2018 / Revised: 13 August 2018 / Accepted: 21 August 2018 / Published: 1 September 2018
Decaying Dark Energy models modify the background evolution of the most common observables, such as the Hubble function, the luminosity distance and the Cosmic Microwave Background temperature–redshift scaling relation. We use the most recent observationally-determined datasets, including Supernovae Type Ia and Gamma Ray Bursts data, along with
and Cosmic Microwave Background temperature versus z
data and the reduced Cosmic Microwave Background parameters, to improve the previous constraints on these models. We perform a Monte Carlo Markov Chain analysis to constrain the parameter space, on the basis of two distinct methods. In view of the first method, the Hubble constant and the matter density are left to vary freely. In this case, our results are compatible with previous analyses associated with decaying Dark Energy models, as well as with the most recent description of the cosmological background. In view of the second method, we set the Hubble constant and the matter density to their best fit values obtained by the Planck
satellite, reducing the parameter space to two dimensions, and improving the existent constraints on the model’s parameters. Our results suggest that the accelerated expansion of the Universe is well described by the cosmological constant, and we argue that forthcoming observations will play a determinant role to constrain/rule out decaying Dark Energy.
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MDPI and ACS Style
de Martino, I. Decaying Dark Energy in Light of the Latest Cosmological Dataset. Symmetry 2018, 10, 372.
de Martino I. Decaying Dark Energy in Light of the Latest Cosmological Dataset. Symmetry. 2018; 10(9):372.
de Martino, Ivan. 2018. "Decaying Dark Energy in Light of the Latest Cosmological Dataset." Symmetry 10, no. 9: 372.
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