Mining the SDSS Archive. I. Photometric Redshifts in the Nearby Universe

In this paper we present a supervised neural network approach to the determination of photometric redshifts. The method, even though of general validity, was fine tuned to match the characteristics of the Sloan Digital Sky Survey (SDSS) and as base of ’a priori ’ knowledge, it exploits the rich wealth of 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, namely: the general galaxy sample (GG) and the luminous red galaxies subsample (LRG). Due to the uneven distribution of measured redshifts in the SDSS spectroscopic subsample, 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), with an accuracy estimated in 97.52%. In the second step two different networks are separately trained on objects belonging to the two redshift ranges. Using a standard Multi Layer Perceptron 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 σz ≃ 0.0208 over the redshift range [0.01, 0.48] and σz ≃ 0.0197 and σz ≃ 0.0238 for the nearby and distant