Estimating Luminosities and Stellar Masses of Galaxies Photometrically without Determining Redshifts

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.