Nonparametric reconstruction of the dark energy equation of state
- Department of Applied Mathematics and Statistics, University of California, Santa Cruz, California 95064 (United States)
- ISR-1, Mailstop D466, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States)
- T-2, Mailstop B285, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States)
- CCS-6, Mailstop F600, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States)
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.
- OSTI ID:
- 21509909
- Journal Information:
- Physical Review. D, Particles Fields, Vol. 82, Issue 10; Other Information: DOI: 10.1103/PhysRevD.82.103502; (c) 2010 American Institute of Physics; ISSN 0556-2821
- Country of Publication:
- United States
- Language:
- English
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