BEAMS: separating the wheat from the chaff in supernova analysis
  We introduce Bayesian Estimation Applied to Multiple Species (BEAMS), an
algorithm designed to deal with parameter estimation when using contaminated
data. We present the algorithm and demonstrate how it works with the help of a
Gaussian simulation. We then apply it to supernova data from the Sloan Digital
Sky Survey (SDSS), showing how the resulting confidence contours of the
cosmological parameters shrink significantly.
