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 can not only be based on constraining a dark energy parameter space , as the errors found depend strongly on the parametrisation 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 } = \pm 0.05 ( 95 \% CL ) at z = 0 and \pm 0.25 at z = 0.7 , with a minimum error of \pm 0.025 at the sweet-spot at z \sim 0.16 , 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 .