Characterizing the contaminating distance distribution for Bayesian supernova cosmology

TL;DR

This paper investigates the distribution of contaminating core collapse supernovae in photometric supernova surveys, emphasizing how their properties and survey limits influence Bayesian contamination modeling for dark energy measurements.

Contribution

It characterizes the contaminating supernova distance distribution using simulations, informing better parameterization for Bayesian supernova cosmology analyses.

Findings

CC luminosity function dominates the contamination distribution.

Redshift increases distortion of the CC distance distribution.

Simulated CC SNe can match SDSS-II data with adjusted luminosity functions.

Abstract

Measurements of the equation of state of dark energy from surveys of thousands of Type Ia Supernovae (SNe Ia) 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 supernova 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 core collapse supernovae. We find that the CC luminosity function 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. (2002) luminosity functions, 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 luminosity functions, coupled with the effects of the survey selection and magnitude-limit, and allow for systematic shifts caused by the parameters of the distance fit.