Smoothing Supernova Data to Reconstruct the Expansion History of the   Universe and its Age
  We propose a non-parametric method of smoothing supernova data over redshift using a Gaussian kernel in order to reconstruct important cosmological quantities including H(z) and w(z) in a model independent manner. This method is shown to be successful in discriminating between different models of dark energy when the quality of data is commensurate with that expected from the future SuperNova Acceleration Probe (SNAP). We find that the Hubble parameter is especially well-determined and useful for this purpose. The look back time of the universe may also be determined to a very high degree of accuracy (\lleq 0.2 %) in this method. By refining the method, it is also possible to obtain reasonable bounds on the equation of state of dark energy. We explore a new diagnostic of dark energy-- the `w-probe'-- which can be calculated from the first derivative of the data. We find that this diagnostic is reconstructed extremely accurately for different reconstruction methods even if \Omega_m is marginalized over. The w-probe can be used to successfully distinguish between $\Lambda$CDM and other models of dark energy to a high degree of accuracy. 
Arman Shafieloo, Ujjaini Alam, Varun Sahni, Alexei A. Starobinsky