Cosmology With Contaminated Samples: Methods of Measuring Dark Energy With Photometrically Classified Pan-Starrs Supernovae

Abstract

The Pan-STARRS (PS1) Medium Deep Survey discovered over 5,000 likely supernovae (SNe) but obtained spectral classifications for just 10% of its SN candidates. We measured spectroscopic host galaxy redshifts for 3,073 of these likely SNe and estimate that ∼1,000 are Type Ia SNe (SNe Ia) with light-curve quality sufficient for a cosmological analysis. We use these data with simulations to determine the impact of core-collapse SN (CC SN) contamination on measurements of the dark energy equation of state parameter, w. Using the method of Bayesian Estimation Applied to Multiple Species (BEAMS), distances to SNe Ia and the contaminating CC SN distribution are simultaneously determined as a function of redshift. We test light-curve based SN classification priors for BEAMS as well as a new classification method that relies upon host galaxy spectra and the association of SN type with host type. By testing several SN classification methods and CC SN parameterizations on 1,000-SN simulations, we conservatively estimate that CC SN contamination gives a systematic error on w (σ CC w ) of 0.014, 30% of the statistical uncertainty. Our best method gives σ CC w = 0.005, just 11% of the statistical uncertainty, but could be affected by incomplete knowledge of the CC SN distribution. Our method determines the SALT2 color and shape coefficients, α and β, with ∼3% bias. Real PS1 SNe without spectroscopic classifications give measurements of w that are within 0.5σ of measurements from PS1 spectroscopically confirmed SNe. Finally, the inferred abundance of bright CC SNe in our sample is greater than expected based on measured CC SN rates and luminosity functions.