Tracy Holsclaw, Ujjaini Alam, Bruno Sanso, Herbie Lee, Katrin Heitmann, Salman Habib and David Higdon
09/17/2009 09:00 AM
Applied Mathematics & Statistics
The major aim of ongoing and upcoming cosmological surveys is to unravel the nature of dark energy. In the absence of a compelling theory to test, a natural approach is to first attempt to characterize the nature of dark energy in detail, the hope being that this will lead to clues about the underlying fundamental theory. A major target in this characterization is the determination of the dynamical properties of the dark energy equation of state w. The discovery of a time variation in w(z) could then lead to insights about the dynamical origin of dark energy. This approach requires a robust and bias-free method for reconstructing w(z) from data, which does not rely on restrictive expansion schemes or assumed functional forms for w(z). We present a new nonparametric reconstruction method for the dark energy equation of state based on Gaussian Process models. 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 GP model approach is very easily extended to include diverse cosmological probes.
