Reconstruction of the Dark Energy equation of state

TL;DR

This paper uses Bayesian analysis to reconstruct the dark energy equation of state from cosmological data, comparing various models and finding indications of possible time dependence, with the node-based approach slightly disfavoured compared to the cosmological constant.

Contribution

It introduces a flexible, node-based Bayesian reconstruction method for the dark energy equation of state and compares it with existing parameterizations using current observational data.

Findings

Indication of possible time dependence in dark energy.

CPL and JBP models are strongly disfavoured.

Node-based reconstruction slightly disfavoured compared to ΛCDM.

Abstract

One of the main challenges of modern cosmology is to investigate the nature of dark energy in our Universe. The properties of such a component are normally summarised as a perfect fluid with a (potentially) time-dependent equation-of-state parameter $w(z)$. We investigate the evolution of this parameter with redshift by performing a Bayesian analysis of current cosmological observations. We model the temporal evolution as piecewise linear in redshift between `nodes', whose $w$-values and redshifts are allowed to vary. The optimal number of nodes is chosen by the Bayesian evidence. In this way, we can both determine the complexity supported by current data and locate any features present in $w(z)$. We compare this node-based reconstruction with some previously well-studied parameterisations: the Chevallier-Polarski-Linder (CPL), the Jassal-Bagla-Padmanabhan (JBP) and the Felice-Nesseris-Tsujikawa (FNT). By comparing the Bayesian evidence for all of these models we find an indication towards possible time-dependence in the dark energy equation-of-state. It is also worth noting that the CPL and JBP models are strongly disfavoured, whilst the FNT is just significantly disfavoured, when compared to a simple cosmological constant $w=-1$. We find that our node-based reconstruction model is slightly disfavoured with respect to the $\Lambda$CDM model.