Journal
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 348, Issue 3, Pages 1038-1046Publisher
OXFORD UNIV PRESS
DOI: 10.1111/j.1365-2966.2004.07429.x
Keywords
methods : data analysis; methods : statistical; galaxies : fundamental parameters; galaxies : photometry; galaxies : statistics
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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.
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