Bayesian Comparison of Interacting Scenarios

TL;DR

This paper uses Bayesian model selection with recent cosmological data to evaluate various dark matter-dark energy interaction models, finding that some are as viable as the standard Lambda-CDM model, while others are disfavored.

Contribution

It provides a comprehensive Bayesian comparison of interacting dark energy models using multiple cosmological datasets, highlighting which models are supported or disfavored by current observations.

Findings

About one-third of models fit data as well as Lambda-CDM.

BAO3 and CMB data can discriminate between models.

Evidence against many interacting models is weak when using BAO2 data.

Abstract

We perform a Bayesian model selection analysis for different classes of phenomenological coupled scenarios of dark matter and dark energy with linear and non-linear interacting terms. We use a combination of some of the latest cosmological data such as type Ia supernovae (SNe Ia), cosmic chronometers (CC), cosmic microwave background (CMB) and two sets of baryon acoustic oscillations measurements, namely, 2-dimensional angular measurements (BAO2) and 3-dimensional angle-averaged measurements (BAO3). We find weak and moderate evidence against two-thirds of the interacting scenarios considered with respect to $\Lambda$CDM when the full joint analysis is considered. About one-third of the models provide a description to the data as good as the one provided by the standard model. Our results also indicate that either SNe Ia, CC or BAO2 data by themselves are not able to distinguish among interacting models or $\Lambda$CDM but the standard BAO3 measurements and the combination with the CMB data are indeed able to discriminate among them. We find that evidence disfavoring interacting models is weaker when we use BAO2 (data claimed to be almost model-independent) instead of the standard BAO3 measurements. These results help select classes of viable and non-viable interacting models in light of current data.