Examining the evidence for dynamical dark energy

TL;DR

This study uses a new Bayesian method to reconstruct dark energy's equation-of-state evolution from multiple cosmological data sets, finding current data mildly favor a dynamical model over the cosmological constant, with future surveys promising better discrimination.

Contribution

Introduces a non-parametric Bayesian reconstruction technique for dark energy evolution using correlated priors, applied to diverse cosmological data sets.

Findings

Cosmological constant remains consistent with data.

Mild preference for a dynamical dark energy model.

Future surveys could distinguish models more clearly.

Abstract

We apply a new non-parametric Bayesian method for reconstructing the evolution history of the equation-of-state $w$ of dark energy, based on applying a correlated prior for $w(z)$, to a collection of cosmological data. We combine the latest supernova (SNLS 3-year or Union2.1), cosmic microwave background, redshift space distortion and the baryonic acoustic oscillation measurements (including BOSS, WiggleZ and 6dF) and find that the cosmological constant appears consistent with current data, but that a dynamical dark energy model which evolves from $w<-1$ at $z\sim0.25$ to $w > -1$ at higher redshift is mildly favored. Estimates of the Bayesian evidences show little preference between the cosmological constant model and the dynamical model for a range of correlated prior choices. Looking towards future data, we find that the best fit models for current data could be well distinguished from the $\Lambda$CDM model by observations such as Planck and Euclid-like surveys.