TY - JOUR
T1 - Galaxy types in the Sloan Digital Sky survey using supervised artificial neural networks
AU - Ball, N. M.
AU - Loveday, J.
AU - Fukugita, M.
AU - Nakamura, O.
AU - Okamura, S.
AU - Brinkmann, J.
AU - Brunner, R. J.
PY - 2004/3/1
Y1 - 2004/3/1
N2 - Supervised artificial neural networks are used to predict useful properties of galaxies in the Sloan Digital Sky Survey, in this instance morphological classifications, spectral types and redshifts. By giving the trained networks unseen data, it is found that correlations between predicted and actual properties are around 0.9 with rms errors of order ten per cent. Thus, given a representative training set, these properties may be reliably estimated for galaxies in the survey for which there are no spectra and without human intervention.
AB - Supervised artificial neural networks are used to predict useful properties of galaxies in the Sloan Digital Sky Survey, in this instance morphological classifications, spectral types and redshifts. By giving the trained networks unseen data, it is found that correlations between predicted and actual properties are around 0.9 with rms errors of order ten per cent. Thus, given a representative training set, these properties may be reliably estimated for galaxies in the survey for which there are no spectra and without human intervention.
KW - Galaxies: fundamental parameters
KW - Galaxies: photometry
KW - Galaxies: statistics
KW - Methods: data analysis
KW - Methods: statistical
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U2 - 10.1111/j.1365-2966.2004.07429.x
DO - 10.1111/j.1365-2966.2004.07429.x
M3 - Review article
AN - SCOPUS:1542327730
SN - 0035-8711
VL - 348
SP - 1038
EP - 1046
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
IS - 3
ER -