Gaussian processes reconstruction of dark energy from observational data

TL;DR

This paper uses Gaussian processes to reconstruct the dark energy equation of state from observational data without assuming a specific model, revealing hints of dynamical dark energy and emphasizing the importance of perturbation data.

Contribution

It introduces a model-independent Gaussian processes approach to reconstruct dark energy, incorporating both background and perturbation data for the first time.

Findings

Both background and perturbation data suggest dynamical dark energy.

Perturbation data better distinguish non-constant dark energy models.

Reconstruction is sensitive to matter density and Hubble constant parameters.

Abstract

In the present paper, we investigate the dark energy equation of state using the Gaussian processes analysis method, without confining a particular parametrization. The reconstruction is carried out by adopting the background data including supernova and Hubble parameter, and perturbation data from the growth rate. It suggests that the background and perturbation data both present a hint of dynamical dark energy. However, the perturbation data have a more promising potential to distinguish non-evolution dark energy including the cosmological constant model. We also test the influence of some parameters on the reconstruction. We find that the matter density parameter $\Omega_{m0}$ has a slight effect on the background data reconstruction, but has a notable influence on the perturbation data reconstruction. While the Hubble constant presents a significant influence on the reconstruction from background data.