Bayesian analysis of CCDM models

Bayesian analysis of CCDM models

Author Jesus, J. F. Google Scholar
Valentim, R. Autor UNIFESP Google Scholar
Andrade-Olivera, F. Google Scholar
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.
Keywords dark energy theory
dark matter theory
supernova type Ia - standard candles
Language English
Sponsor Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Grant number FAPESP: 2017/05859-0
FAPESP: 2013/26258-4
FAPESP: 2016/09831-0
Date 2017
Published in Journal Of Cosmology And Astroparticle Physics. Bristol, v. , n. 9, p. -, 2017.
ISSN 1475-7516 (Sherpa/Romeo, impact factor)
Publisher Iop Publishing Ltd
Extent -
Access rights Closed access
Type Article
Web of Science ID WOS:000411456000002

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