Comparing the light curves of simulated Type Ia Supernovae with
  observations using data-driven models
  We propose a robust, quantitative method to compare the synthetic light
curves of a Type Ia Supernova (SNIa) explosion model with a large set of
observed SNeIa, and derive a figure of merit for the explosion model's
agreement with observations. The synthetic light curves are fit with the
data-driven model SALT2 which returns values for stretch, color, and magnitude
at peak brightness, as well as a goodness-of-fit parameter. Each fit is
performed multiple times with different choices of filter bands and epoch range
in order to quantify the systematic uncertainty on the fitted parameters. We
use a parametric population model for the distribution of observed SNIa
parameters from large surveys, and extend it to represent red, dim, and bright
outliers found in a low-redshift SNIa data set. We discuss the potential
uncertainties of this population model and find it to be reliable given the
current uncertainties on cosmological parameters. Using our population model,
we assign each set of fitted parameters a likelihood of being observed in
nature, and a figure of merit based on this likelihood. We define a second
figure of merit based on the quality of the light curve fit, and combine the
two measures into an overall figure of merit for each explosion model. We
compute figures of merit for a variety of 1D, 2D and 3D explosion models and
show that our evaluation method allows meaningful inferences across a wide
range of light curve quality and fitted parameters.
