Reconstruction of dark energy and expansion dynamics using Gaussian processes
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Date
2012
Authors
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Publisher
IOP Publishing
Abstract
An important issue in cosmology is reconstructing the effective dark energy equation
of state directly from observations. With few physically motivated models, future dark
energy studies cannot only be based on constraining a dark energy parameter space, as
the errors found depend strongly on the parametrisation considered. We present a new
non-parametric approach to reconstructing the history of the expansion rate and dark energy
using Gaussian Processes, which is a fully Bayesian approach for smoothing data. We
present a pedagogical introduction to Gaussian Processes, and discuss how it can be used
to robustly differentiate data in a suitable way. Using this method we show that the Dark
Energy Survey - Supernova Survey (DES) can accurately recover a slowly evolving equation
of state to w = ±0.05 (95% CL) at z = 0 and ±0.25 at z = 0.7, with a minimum error
of ±0.025 at the sweet-spot at z 0.16, provided the other parameters of the model are
known. Errors on the expansion history are an order of magnitude smaller, yet make no
assumptions about dark energy whatsoever.
Description
Keywords
Physics, Dark energy, Cosmology, Astronomy, Gaussian processes
Citation
Seikel, M., & Clarkson, C. (2012). Reconstruction of dark energy and expansion dynamics using Gaussian processes. Journal of Cosmology and Astroparticle Physics. 6. 10.1088/1475-7516/2012/06/036