CHARACTERIZING THE CONTAMINATING DISTANCE DISTRIBUTION FOR BAYESIAN SUPERNOVA COSMOLOGY

Abstract

Measurements of the equation of state of dark energy from surveys of thousands of Type Ia Supernovae will be limited by spectroscopic follow-up and must therefore rely on photometric identification, increasing the chance that the sample is contaminated by core collapse supernovae (CC SNe). Bayesian methods for SN cosmology can remove contamination bias while maintaining high statistical precision but are sensitive to the choice of parameterization of the contaminating distance distribution. We use simulations to investigate the form of the contaminating distribution and its dependence on the absolute magnitudes, light curve shapes, colors, extinction, and redshifts of CC SNe. We find that the CC luminosity function (LF) dominates the distance distribution function, but its shape is increasingly distorted as the redshift increases and more CC SNe fall below the survey magnitude limit. The shapes and colors of the CC light curves generally shift the distance distribution, and their effect on the CC distances is correlated. We compare the simulated distances to the first year results of the SDSS-II SN survey and find that the SDSS distance distributions can be reproduced with simulated CC SNe that are ∼1 mag fainter than the standard Richardson et al. LFs, which do not produce a good fit. To exploit the full power of the Bayesian parameter estimation method, parameterization of the contaminating distribution should be guided by the current knowledge of the CC LFs, coupled with the effects of the survey selection and magnitude limit, and allow for systematic shifts caused by the parameters of the distance fit.