Journal
PHYSICAL REVIEW D
Volume 82, Issue 10, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevD.82.103502
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Funding
- Los Alamos National Laboratory (LANL) Institute for Scalable Scientific Data Management
- DOE [W-7405-ENG-36]
- Los Alamos National Laboratory
- NASA
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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.
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