Characterizing and Propagating Modeling Uncertainties in
  Photometrically-Derived Redshift Distributions
  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.
