Reconstruction of dark energy and expansion dynamics using Gaussian processes

Abstract

An important issue in cosmology is reconstructing the effective darkenergy equation of state directly from observations. With fewphysically motivated models, future dark energy studies cannot only bebased on constraining a dark energy parameter space, as the errorsfound depend strongly on the parametrisation considered. We present anew non-parametric approach to reconstructing the history of theexpansion rate and dark energy using Gaussian Processes, which is afully Bayesian approach for smoothing data. We present a pedagogicalintroduction to Gaussian Processes, and discuss how it can be used torobustly differentiate data in a suitable way. Using this method weshow that the Dark Energy Survey - Supernova Survey (DES) canaccurately recover a slowly evolving equation of state to σw = ±0.05 (95% CL) at z = 0 and ±0.25 at z = 0.7, with a minimumerror of ±0.025 at the sweet-spot at z ∼ 0.16, provided theother parameters of the modelare known. Errors on the expansion history are an order of magnitudesmaller, yet make no assumptions about dark energy whatsoever. A codefor calculating functions and their first three derivatives usingGaussian processes has been developed and is available fordownload.