Non-parametric Reconstruction of Dark Energy Equation of State from Diverse Datasets
Alam, Ujjaini (Los Alamos National Laboratory)

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 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). In this talk I outline a new, nonparametric method for reconstructing w(z) based on Gaussian Process modeling. Using this method on diverse datasets such as Type Ia supernovae and baryon acoustic oscillations, it is possible to obtain strong constraints on non-trivial behaviour of w(z).