Towards the Future of Supernova Cosmology
  For future surveys, spectroscopic follow-up for all supernovae will be
extremely difficult. However, one can use light curve fitters, to obtain the
probability that an object is a Type Ia. One may consider applying a
probability cut to the data, but we show that the resulting non-Ia
contamination can lead to biases in the estimation of cosmological parameters.
A different method, which allows the use of the full dataset and results in
unbiased cosmological parameter estimation, is Bayesian Estimation Applied to
Multiple Species (BEAMS). BEAMS is a Bayesian approach to the problem which
includes the uncertainty in the types in the evaluation of the posterior. Here
we outline the theory of BEAMS and demonstrate its effectiveness using both
simulated datasets and SDSS-II data. We also show that it is possible to use
BEAMS if the data are correlated, by introducing a numerical marginalisation
over the types of the objects. This is largely a pedagogical introduction to
BEAMS with references to the main BEAMS papers.
