Reconstructing the interaction between dark energy and dark matter using Gaussian Processes

TL;DR

This paper introduces a Bayesian nonparametric method using Gaussian processes to reconstruct dark energy-dark matter interaction from supernova data, revealing degeneracies with the dark energy equation of state.

Contribution

It presents a novel, model-independent approach to reconstruct dark sector interactions directly from observational data using Gaussian processes.

Findings

Interaction consistent with no interaction for w=-1

Interaction detectable if w deviates from -1 at 95% confidence

Degeneracy between dark energy equation of state and interaction strength

Abstract

We present a nonparametric approach to reconstruct the interaction between dark energy and dark matter directly from SNIa Union 2.1 data using Gaussian processes, which is a fully Bayesian approach for smoothing data. In this method, once the equation of state ($w$) of dark energy is specified, the interaction can be reconstructed as a function of redshift. For the decaying vacuum energy case with $w=-1$, the reconstructed interaction is consistent with the standard $\Lambda$CDM model, namely, there is no evidence for the interaction. This also holds for the constant $w$ cases from $-0.9$ to $-1.1$ and for the Chevallier-Polarski-Linder (CPL) parametrization case. If the equation of state deviates obviously from $-1$, the reconstructed interaction exists at $95\%$ confidence level. This shows the degeneracy between the interaction and the equation of state of dark energy when they get constraints from the observational data.