Reconstruction of dark energy and expansion dynamics using Gaussian processes

Abstract

An important issue in cosmology is reconstructing the effective dark energy equation
\nof state directly from observations. With few physically motivated models, future dark
\nenergy studies cannot only be based on constraining a dark energy parameter space, as
\nthe errors found depend strongly on the parametrisation considered. We present a new
\nnon-parametric approach to reconstructing the history of the expansion rate and dark energy
\nusing Gaussian Processes, which is a fully Bayesian approach for smoothing data. We
\npresent a pedagogical introduction to Gaussian Processes, and discuss how it can be used
\nto robustly differentiate data in a suitable way. Using this method we show that the Dark
\nEnergy Survey - Supernova Survey (DES) can accurately recover a slowly evolving equation
\nof state to w = ±0.05 (95% CL) at z = 0 and ±0.25 at z = 0.7, with a minimum error
\nof ±0.025 at the sweet-spot at z 0.16, provided the other parameters of the model are
\nknown. Errors on the expansion history are an order of magnitude smaller, yet make no
\nassumptions about dark energy whatsoever.