Dark energy by natural evolution: Constraining dark energy using Approximate Bayesian Computation

TL;DR

This paper uses Approximate Bayesian Computation to analyze late-time cosmological data, finding that dynamical dark energy models are favored over the standard Lambda Cold Dark Matter scenario, with robustness across different priors.

Contribution

It introduces a biology-inspired ABC approach to constrain dark energy, providing robust evidence favoring dynamical dark energy over LambdaCDM.

Findings

Dynamical dark energy is preferred over LambdaCDM.

The algorithm favors low Hubble constant values.

Results are consistent with traditional MCMC analyses.

Abstract

We look at dark energy from a biology inspired viewpoint by means of the Approximate Bayesian Computation (ABC) and late time cosmological observations. We find that dynamical dark energy comes out on top, or in the ABC language naturally selected, over the standard $\Lambda$CDM cosmological scenario. We confirm this conclusion is robust to whether baryon acoustic oscillations and Hubble constant priors are considered. Our results show that the algorithm prefers low values of the Hubble constant, consistent or at least a few standard deviation away from the cosmic microwave background estimate, regardless of the priors taken initially in each model. This supports the result of the traditional MCMC analysis and could be viewed as strengthening evidence for dynamical dark energy being a more favorable model of late time cosmology.