Nonparametric dark energy reconstruction from supernova data.

Phys Rev Lett

Department of Applied Mathematics and Statistics, University of California, Santa Cruz, California 95064, USA.

Published: December 2010

AI Article Synopsis

  • Understanding the accelerated expansion of the Universe is a major challenge in physics, primarily linked to the mysterious role of dark energy.
  • Researchers are focusing on the equation of state parameter w(z) to gather clues about dark energy's origin through its redshift evolution.
  • A new nonparametric method using Gaussian process modeling and Markov chain Monte Carlo sampling has been developed to accurately reconstruct w(z), applying it to recent supernova data to trace its history up to redshift z=1.5.

Article 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.

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http://dx.doi.org/10.1103/PhysRevLett.105.241302DOI Listing

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