Reconstruction of dark energy and expansion dynamics using Gaussian processes

TL;DR

This paper introduces a Bayesian non-parametric method using Gaussian Processes to reconstruct dark energy and expansion history from observational data, providing precise, model-independent insights into cosmic acceleration.

Contribution

It presents a novel application of Gaussian Processes for non-parametric reconstruction of dark energy and expansion history, avoiding assumptions of specific models.

Findings

Accurately recovers a slowly evolving dark energy equation of state with small errors.

Errors on expansion history are significantly reduced without assuming dark energy models.

Provides an open-source code for derivative calculations using Gaussian Processes.

Abstract

An important issue in cosmology is reconstructing the effective dark energy equation of state directly from observations. With few physically motivated models, future dark energy studies cannot only be based on constraining a dark energy parameter space, as the errors found depend strongly on the parameterisation considered. We present a new non-parametric approach to reconstructing the history of the expansion rate and dark energy using Gaussian Processes, which is a fully Bayesian approach for smoothing data. We present a pedagogical introduction to Gaussian Processes, and discuss how it can be used to robustly differentiate data in a suitable way. Using this method we show that the Dark Energy Survey - Supernova Survey (DES) can accurately recover a slowly evolving equation of state to sigma_w = +-0.04 (95% CL) at z=0 and +-0.2 at z=0.7, with a minimum error of +-0.015 at the sweet-spot at z~0.14, provided the other parameters of the model are known. Errors on the expansion history are an order of magnitude smaller, yet make no assumptions about dark energy whatsoever. A code for calculating functions and their first three derivatives using Gaussian processes has been developed and is available for download at http://www.acgc.uct.ac.za/~seikel/GAPP/index.html .