Evolution of dark energy reconstructed from the latest observations

TL;DR

This paper reconstructs the evolution of dark energy density using a nonparametric Bayesian approach from recent data, revealing evidence for dynamical dark energy features like oscillations and negative densities, challenging the standard cosmological model.

Contribution

It introduces a nonparametric Bayesian method to reconstruct dark energy evolution, providing the first Bayesian evidence for dynamical dark energy features.

Findings

3.7σ preference for evolving dark energy density

Oscillatory behavior around ΛCDM at z<0.7

Evidence supports dynamical dark energy at 2.5σ significance

Abstract

We reconstruct evolution of the dark energy (DE) density using a nonparametric Bayesian approach from a combination of latest observational data. We caution against parameterizing DE in terms of its equation of state as it can be singular in modified gravity models, and using it introduces a bias preventing negative effective DE densities. We find a $3.7\sigma$ preference for an evolving effective DE density with interesting features. For example, it oscillates around the $\Lambda$CDM prediction at $z\lesssim0.7$, and could be negative at $z\gtrsim2.3$; dark energy can be pressure-less at multiple redshifts, and a short period of cosmic deceleration is allowed at $0.1 \lesssim z\lesssim 0.2$. We perform the reconstruction for several choices of the prior, as well as a evidence-weighted reconstruction. We find that some of the dynamical features, such as the oscillatory behaviour of the DE density, are supported by the Bayesian evidence, which is a first detection of a dynamical DE with a positive Bayesian evidence. The evidence-weighted reconstruction prefers a dynamical DE at a $(2.5\pm0.06)\sigma$ significance level.