Bayesian analysis of CCDM Models

TL;DR

This paper evaluates six spatially flat Cold Dark Matter creation models using statistical criteria and supernova data, finding slight preference for the JO model but no definitive exclusion of others.

Contribution

It applies Bayesian and information criteria to compare CCDM models against supernova data, highlighting the viability of certain models over others.

Findings

JO model is slightly favored over ΛCDM.

Some models are discarded due to poor fit or excessive parameters.

No model can be conclusively discarded based on current data.

Abstract

Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, leads to negative creation pressure, 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 tools, at light of SN Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These approaches 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 from poor fitting, either from excess of free parameters.