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.
