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

Authors :: D'Abrusco Raffaele
Brescia Massimo
De Filippis Elisabetta
Tagliaferri Roberto
Longo Giuseppe
Paolillo Maurizio
Staiano Antonino
D'Abrusco, Raffaele
Staiano, Antonino
Longo, Giuseppe
Brescia, Massimo
Paolillo, Maurizio
De Filippis, Elisabetta
Tagliaferri, Roberto
Staiano, A.
Brescia, M.
DE FILIPPIS, E.
Tagliaferri, R.
Publication Year :: 2007
Abstract: 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<br />Comment: 45 pages, 14 figures, accepted for publication is the Astrophysical Journal

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