From model dynamics to oscillating dark energy parameterisation

TL;DR

This paper proposes an oscillating dark energy model based on a non-minimally coupled scalar field, showing it fits observational data better than linear models, using Bayesian model selection.

Contribution

It introduces a new oscillatory dark energy parameterisation derived from scalar field dynamics and compares it with existing models using observational data.

Findings

Oscillatory model is favored over linear models by Bayesian analysis.

The model's equation of state oscillates with the scale factor.

Data supports dynamic, oscillating dark energy over a cosmological constant.

Abstract

We develop here a relatively simple description of dark energy based on the dynamics of non-minimally coupled to gravity phantom scalar field which, in limit, corresponds to cosmological constant. The dark energy equation of state, obtained directly from the dynamics of the model, turns out to be an oscillatory function of the scale factor. This parameterisation is compared to other possible dark energy parameterisations, among them, the most popular one, linear in the scale factor. We use the Bayesian framework for model selection and make a comparison in the light of SN Ia, CMB shift parameter, BAO A parameter, observational H(z) and growth rate function data. We find that there is evidence to favour a parameterisation with oscillations over {\it a priori} assumed linear one.