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Understanding the origin of the accelerated expansion of the Universe posesone of the greatest challenges in physics today. Lacking a compellingfundamental theory to test, observational efforts are targeted at a bettercharacterization of the underlying cause. If a new form of mass-energy, darkenergy, is driving the acceleration, the redshift evolution of the equation ofstate parameter w(z) will hold essential clues as to its origin. To bestexploit data from observations it is necessary to develop a robust and accuratereconstruction approach, with controlled errors, for w(z). We introduce a new,nonparametric method for solving the associated statistical inverse problembased on Gaussian Process modeling and Markov chain Monte Carlo sampling.Applying this method to recent supernova measurements, we reconstruct thecontinuous history of w out to redshift z=1.5.

Nonparametric Dark Energy Reconstruction from Supernova Data