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We present results from a study of the photometric redshift performance ofthe Dark Energy Survey (DES), using the early data from a Science Verification(SV) period of observations in late 2012 and early 2013 that providedscience-quality images for almost 200 sq.~deg.~at the nominal depth of thesurvey. We assess the photometric redshift performance using about 15000galaxies with spectroscopic redshifts available from other surveys. Thesegalaxies are used, in different configurations, as a calibration sample, andphoto-$z$'s are obtained and studied using most of the existing photo-$z$codes. A weighting method in a multi-dimensional color-magnitude space isapplied to the spectroscopic sample in order to evaluate the photo-$z$performance with sets that mimic the full DES photometric sample, which is onaverage significantly deeper than the calibration sample due to the limiteddepth of spectroscopic surveys. Empirical photo-$z$ methods using, forinstance, Artificial Neural Networks or Random Forests, yield the bestperformance in the tests, achieving core photo-$z$ resolutions $\sigma_{68}\sim 0.08$. Moreover, the results from most of the codes, including templatefitting methods, comfortably meet the DES requirements on photo-$z$performance, therefore, providing an excellent precedent for future DES datasets.

Photometric redshift analysis in the Dark Energy Survey Science Verification data