  We present a supervised neural network approach to the determination of
photometric redshifts. The method was tuned to match the characteristics of the
Sloan Digital Sky Survey and it exploits the spectroscopic redshifts provided
by this unique survey. In order to train, validate and test the networks we
used two galaxy samples drawn from the SDSS spectroscopic dataset: the general
galaxy sample (GG) and the luminous red galaxies subsample (LRG). The method
consists of a two steps approach. In the first step, objects are classified in
nearby (z<0.25) and distant (0.25<z<0.50). In the second step two different
networks are separately trained on objects belonging to the two redshift
ranges. Using a standard MLP operated in a Bayesian framework, the optimal
architectures were found to require 1 hidden layer of 24 (24) and 24 (25)
neurons for the GG (LRG) sample. The presence of systematic deviations was then
corrected by interpolating the resulting redshifts. The final results on the GG
dataset give a robust sigma_z = 0.0208 over the redshift range [0.01, 0.48] and
sigma_z = 0.0197 and sigma_z = 0.0238 for the nearby and distant samples
respectively. For the LRG subsample we find a robust sigma_z = 0.0164 over the
whole range, and sigma_z = 0.0160, sigma_z = 0.0183 for the nearby and distant
samples respectively. After training, the networks have been applied to all
objects in the SDSS Table GALAXY matching the same selection criteria adopted
to build the base of knowledge, and photometric redshifts for ca. 30 million
galaxies having z<0.5 were derived. A catalogue containing photometric
redshifts for the LRG subsample was also produced.
