Assessment of Systematic Uncertainties in the Cosmological Analysis of the SDSS Supernovae Photometric Sample

Abstract

Abstract Improvements to the precision of measurements of cosmological parameters with Type Ia supernovae (SNe Ia) are expected to come from large photometrically identified (photometric) supernova (SN) samples. Here we reanalyze the Sloan Digital Sky Survey (SDSS) photometric SN sample, with roughly 700 high-quality, likely but unconfirmed SNe Ia light curves, to develop new analysis tools aimed at evaluating systematic uncertainties on the dark energy equation-of-state parameter w. Since we require a spectroscopically measured host-galaxy redshift for each SN, we determine the associated selection efficiency of host galaxies in order to simulate bias corrections. We determine that the misassociation rate of host galaxies is 0.6%; ignoring this effect in simulated bias corrections leads to a w-bias of Δw = +0.0007, where w is evaluated from SNe Ia and priors from measurements of baryon acoustic oscillations and the cosmic microwave background. We assess the uncertainty in our modeling of the host-galaxy selection efficiency and find the associated w uncertainty to be −0.0072. Finally, we explore new core-collapse (CC) models in simulated training samples and find that adjusting the CC luminosity distribution to be in agreement with previous Pan-STARRS analyses yields a better match to the SDSS data. The impact of ignoring this adjustment is Δw = −0.0109; the impact of replacing the new CC models with those used by Pan-STARRS is Δw = −0.0028. These systematic uncertainties are subdominant to the statistical constraints from the SDSS sample, but must be considered in future photometric analyses of large SN samples such as those from the Dark Energy Survey (DES), the Large Synoptic Survey Telescope (LSST), and the Wide Field Infrared Survey Telescope (WFIRST).