  Large direct-imaging surveys usually use a template-fitting technique to
estimate photometric redshifts for galaxies, which are then applied to derive
important galaxy properties such as luminosities and stellar masses. These
estimates can be noisy and suffer from systematic biases because of the
possible mis-selection of templates and the propagation of the photometric
redshift uncertainty. We introduce an algorithm, the Direct Empirical
Photometric method (DEmP), which can be used to directly estimate these
quantities using training sets, bypassing photometric redshift determination.
DEmP also applies two techniques to minimize the effects arising from the
non-uniform distribution of training-set galaxy redshifts from a flux-limited
sample. First, for each input galaxy, fitting is performed using a subset of
the training-set galaxies with photometry and colors closest to those of the
input galaxy. Second, the training set is artificially resampled to produce a
flat distribution in redshift, or other properties, e.g., luminosity. To test
the performance of DEmP, we use a 4-filter-band mock catalog to examine its
ability to recover redshift, luminosity, stellar mass, and luminosity and
stellar-mass functions. We also compare the results to those from two publicly
available template-fitting methods, finding that the DEmP algorithm outperforms
both. We find resampling the training set to have a uniform redshift
distribution produces the best results not only in photometric redshift, but
also in estimating luminosity and stellar mass. The DEmP method is especially
powerful in estimating quantities such as near-IR luminosities and stellar mass
using only data from a small number of optical bands.
