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 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 σ w = ±0.05 (95% CL) at z = 0 and ±0.25 at z = 0.7, with a minimum error of ±0.025 at the sweet-spot at z ∼ 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.

Aims. We test the isotropy of the expansion of the Universe by estimating the hemispherical anisotropy of supernova type Ia (SN Ia) Hubble diagrams at low redshifts (z < 0.2). Methods. We compare the best fit Hubble diagrams in pairs of hemispheres and search for the maximal asymmetric orientation. For an isotropic Universe, we expect only a small asymmetry due to noise and the presence of nearby structures. This test does not depend on the assumed content of the Universe, the assumed model of gravity, or the spatial curvature of the Universe. The expectation for possible fluctuations due to large scale structure is evaluated for the Λ cold dark matter (ΛCDM) model and is compared to the supernova data from the Constitution set for four different light curve fitters, thus allowing a study of the systematic effects. Results. The expected order of magnitude of the hemispherical asymmetry of the Hubble expansion agrees with the observed one. The direction of the Hubble asymmetry is established at 95% confidence level (C.L.) using both, the MLCS2k2 and the SALT II light curve fitter. The highest expansion rate is found towards ( , b) ≈ (−35 • , −19 • ), which agrees with directions reported by other studies. Its amplitude is not in contradiction to expectations from the ΛCDM model. The measured Hubble anisotropy is ΔH/H ∼ 0.026. With 95% C.L. the expansion asymmetry is ΔH/H < 0.038.

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Gaussian processes are a fully Bayesian smoothing technique that allows for the reconstruction of a function and its derivatives directly from observational data, without assuming a specific model or choosing a parameterization. This is ideal for constraining dark energy because physical models are generally phenomenological and poorly motivated. Model-independent constraints on dark energy are an especially important alternative to parameterized models, as the priors involved have an entirely different source so can be used to check constraints formulated from models or parameterizations. A critical prior for Gaussian process reconstruction lies in the choice of covariance function. We show how the choice of covariance function affects the result of the reconstruction, and present a choice which leads to reliable results for present day supernovae data. We also introduce a method to quantify deviations of a model from the Gaussian process reconstructions.