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 (SN Ia) explosion model with a large set of observed SNe Ia, 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 SN Ia parameters from large surveys, and extend it to represent red, dim, and bright outliers found in a low-redshift SN Ia 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 one-, two-, and three-dimensional explosion models and show that our evaluation method allows meaningful inferences across a wide range of light curve quality and fitted parameters.