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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Bayesian analysis of CCDM models

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Author(s):
Jesus, J. F. ; Valentim, R. ; Andrade-Olivera, F.
Total Authors: 3
Document type: Journal article
Source: Journal of Cosmology and Astroparticle Physics; n. 9 SEP 2017.
Web of Science Citations: 0
Abstract

Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/Lambda CDM model, however, neither of these, nor Gamma = 3 alpha H-0 model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed. (AU)

FAPESP's process: 16/09831-0 - Neutron stars populations: Bayesian statistics tools
Grantee:Rodolfo Valentim da Costa Lima
Support type: Regular Research Grants
FAPESP's process: 17/05859-0 - The accelerating universe: nature and tests of dark energy and dark matter
Grantee:José Fernando de Jesus
Support type: Regular Research Grants
FAPESP's process: 13/26258-4 - Superdense matter in the universe
Grantee:Manuel Máximo Bastos Malheiro de Oliveira
Support type: Research Projects - Thematic Grants