Nonparametric Reconstruction of the Dark Energy Equation of State from
  Diverse Data Sets
  The cause of the accelerated expansion of the Universe poses one of the most
fundamental questions in physics today. In the absence of a compelling theory
to explain the observations, a first task is to develop a robust phenomenology.
If the acceleration is driven by some form of dark energy, then, the
phenomenology is determined by the dark energy equation of state w. A major aim
of ongoing and upcoming cosmological surveys is to measure w and its time
dependence at high accuracy. Since w(z) is not directly accessible to
measurement, powerful reconstruction methods are needed to extract it reliably
from observations. We have recently introduced a new reconstruction method for
w(z) based on Gaussian process modeling. This method can capture nontrivial
time-dependences in w(z) and, most importantly, it yields controlled and
unbaised error estimates. In this paper we extend the method to include a
diverse set of measurements: baryon acoustic oscillations, cosmic microwave
background measurements, and supernova data. We analyze currently available
data sets and present the resulting constraints on w(z), finding that current
observations are in very good agreement with a cosmological constant. In
addition we explore how well our method captures nontrivial behavior of w(z) by
analyzing simulated data assuming high-quality observations from future
surveys. We find that the baryon acoustic oscillation measurements by
themselves already lead to remarkably good reconstruction results and that the
combination of different high-quality probes allows us to reconstruct w(z) very
reliably with small error bounds.
