Nonparametric Dark Energy Reconstruction from Supernova Data
Abstract
Understanding the origin of the accelerated expansion of the Universe poses one of the greatest challenges in physics today. Lacking a compelling fundamental theory to test, observational efforts are targeted at a better characterization of the underlying cause. If a new form of mass-energy, dark energy, is driving the acceleration, the redshift evolution of the equation of state parameter w(z) will hold essential clues as to its origin. To best exploit data from observations it is necessary to develop a robust and accurate reconstruction approach, with controlled errors, for w(z). We introduce a new, nonparametric method for solving the associated statistical inverse problem based on Gaussian Process modeling and Markov chain Monte Carlo sampling. Applying this method to recent supernova measurements, we reconstruct the continuous history of w out to redshift z=1.5.
Keywords
Cite
@article{arxiv.1011.3079,
title = {Nonparametric Dark Energy Reconstruction from Supernova Data},
author = {Tracy Holsclaw and Ujjaini Alam and Bruno Sanso and Herbert Lee and Katrin Heitmann and Salman Habib and David Higdon},
journal= {arXiv preprint arXiv:1011.3079},
year = {2010}
}
Comments
4 pages, 2 figures, accepted for publication in Physical Review Letters