Optimal Extraction of Cosmological Information from Supernova Data in   the Presence of Calibration Uncertainties
  We present a new technique to extract the cosmological information from high-redshift supernova data in the presence of calibration errors and extinction due to dust. While in the traditional technique the distance modulus of each supernova is determined separately, in our approach we determine all distance moduli at once, in a process that achieves a significant degree of self-calibration. The result is a much reduced sensitivity of the cosmological parameters to the calibration uncertainties. As an example, for a strawman mission similar to that outlined in the SNAP satellite proposal, the increased precision obtained with the new approach is roughly equivalent to a factor of five decrease in the calibration uncertainty. 
Alex G. Kim and Ramon Miquel