MEASURING TYPE IA SUPERNOVA POPULATIONS OF STRETCH AND COLOR AND PREDICTING DISTANCE BIASES

Abstract

Simulations of Type Ia supernovae (SNe Ia) surveys are a critical tool for correcting biases in the analysis of SNe Ia to infer cosmological parameters. Large-scale Monte Carlo simulations include a thorough treatment of observation history, measurement noise, intrinsic scatter models, and selection effects. In this Letter, we improve simulations with a robust technique to evaluate the underlying populations of SN Ia color and stretch that correlate with luminosity. In typical analyses, the standardized SN Ia brightness is determined from linear “Tripp” relations between the light curve color and luminosity and between stretch and luminosity. However, this solution produces Hubble residual biases because intrinsic scatter and measurement noise result in measured color and stretch values that do not follow the Tripp relation. We find a 10 σ bias (up to 0.3 mag) in Hubble residuals versus color and 5 σ bias (up to 0.2 mag) in Hubble residuals versus stretch in a joint sample of 920 spectroscopically confirmed SN Ia from PS1, SNLS, SDSS, and several low- z surveys. After we determine the underlying color and stretch distributions, we use simulations to predict and correct the biases in the data. We show that removing these biases has a small impact on the low- z sample, but reduces the intrinsic scatter σ int from 0.101 to 0.083 in the combined PS1, SNLS, and SDSS sample. Past estimates of the underlying populations were too broad, leading to a small bias in the equation of state of dark energy w of Δ w = 0.005.