Nonparametric reconstruction of the dark energy equation of state

A basic aim of ongoing and upcoming cosmological surveys is to unravel the mystery of dark energy. In the absence of a compelling theory to test, a natural approach is to better characterize the properties of dark energy in search of clues that can lead to a more fundamental understanding. One way to view this characterization is the improved determination of the redshift-dependence of the dark energy equation of state parameter, $w(z)$. To do this requires a robust and bias-free method for reconstructing $w(z)$ from data that does not rely on restrictive expansion schemes or assumed functional forms for $w(z)$. We present a new nonparametric reconstruction method that solves for $w(z)$ as a statistical inverse problem, based on a Gaussian process representation. This method reliably captures nontrivial behavior of $w(z)$ and provides controlled error bounds. We demonstrate the power of the method on different sets of simulated supernova data; the approach can be easily extended to include diverse cosmological probes.