Nonparametric reconstruction of the dark energy equation of state
- Los Alamos National Laboratory
- UC SANTA CRUZ
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 non parametric 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 demollstrate 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.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 970030
- Report Number(s):
- LA-UR-09-05888; LA-UR-09-5888; PRVDAQ; TRN: US201002%%1071
- Journal Information:
- Physical Review. D, Particles, Fields, Gravitation and Cosmology, Journal Name: Physical Review. D, Particles, Fields, Gravitation and Cosmology; ISSN 1550-7998
- Country of Publication:
- United States
- Language:
- English
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Nonparametric reconstruction of the dark energy equation of state from diverse data sets
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