Characterizing and Propagating Modeling Uncertainties in Photometrically-Derived Redshift Distributions

Abstract

The uncertainty in the redshift distributions of galaxies has a significant potential impact on the cosmological parameter values inferred from multi-band imaging surveys. The accuracy of the photometric redshifts measured in these surveys depends not only on the quality of the flux data, but also on a number of modeling assumptions that enter into both the training set and SED fitting methods of photometric redshift estimation. In this work we focus on the latter, considering two types of modeling uncertainties: uncertainties in the SED template set and uncertainties in the magnitude and type priors used in a Bayesian photometric redshift estimation method. We find that SED template selection effects dominate over magnitude prior errors. We introduce a method for parameterizing the resulting ignorance of the redshift distributions, and for propagating these uncertainties to uncertainties in cosmological parameters.