Open AccessMining the SDSS Archive. I. Photometric Redshifts in the Nearby Universe
Author(s) -
R. D’Abrusco,
Antonino Staiano,
G. Longo,
M. Brescia,
M. Paolillo,
E. De Filippis,
Roberto Tagliaferri
Publication year - 2007
Publication title -
the astrophysical journal
Language(s) - English
Resource type - Journals
eISSN - 1538-4357
pISSN - 0004-637X
DOI - 10.1086/518020
Subject(s) - redshift , galaxy , astrophysics , photometric redshift , physics , sky , universe , artificial neural network , sample (material) , matching (statistics) , range (aeronautics) , cosmology , computer science , set (abstract data type) , artificial intelligence , statistics , mathematics , thermodynamics , programming language , materials science , composite material
We present a supervised neural network approach to the determination ofphotometric redshifts. The method was tuned to match the characteristics of theSloan Digital Sky Survey and it exploits the spectroscopic redshifts providedby this unique survey. In order to train, validate and test the networks weused two galaxy samples drawn from the SDSS spectroscopic dataset: the generalgalaxy sample (GG) and the luminous red galaxies subsample (LRG). The methodconsists of a two steps approach. In the first step, objects are classified innearby (z<0.25) and distant (0.25
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom