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A Bayesian comparison between $$\Lambda $$CDM and phenomenologically emergent dark energy models

M. Rezaei, T. Naderi, M. Malekjani, A. Mehrabi

2020The European Physical Journal C44 citationsDOIOpen Access PDF

Abstract

Abstract In this work we examine the recently proposed phenomenological emergent dark energy (PEDE) model by [1], using the latest observational data in both expansion and perturbation levels. Applying the statistical Bayesian evidence as well as the AIC and BIC information criteria, we compare the PEDE model with the concordance $$\Lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Λ</mml:mi></mml:math> CDM model in both flat and non-flat universes. We combine the observational datasets as (i) expansion data (except CMB), (ii) expansion data (including CMB) and (iii) expansion data jointed to the growth rate dataset. Our statistical results show that the flat- $$\Lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Λ</mml:mi></mml:math> CDM model is still the best model. In the case of expansion data (including CMB), we observe that the flat- PEDE model is well consistent with observations as well as the concordance $$\Lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Λ</mml:mi></mml:math> CDM universe. While in the cases of (i) and (iii), the PEDE models in both of the flat and non-flat geometries are not favored. In particular, we see that in the perturbation level the PEDE model can not fit the observations as equally as standard $$\Lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Λ</mml:mi></mml:math> CDM cosmology. As the ability of the model, we show that the PEDE models can alleviate the tension of Hubble constant value appearing between the local observations and Planck inferred estimation in standard cosmology.

Topics & Concepts

Dark energyStatistical modelHubble's lawStatistical physicsPhysicsModel selectionPlanckBayesian probabilityPerturbation (astronomy)Cosmological perturbation theoryMathematicsBayesian inferenceConstant (computer programming)StatisticsBayesian statisticsFrequentist inferenceCosmology and Gravitation TheoriesStatistical Mechanics and EntropyGalaxies: Formation, Evolution, Phenomena
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