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.
One study REF , which took a supervised approach, trained an ANN to predict specific properties of galaxies, namely morphological classifications and redshifts, which proved reliable.
Galaxy Types in the Sloan Digital Sky Survey Using Supervised Artificial Neural Networks
Mon.Not.Roy.Astron.Soc.348:1038,2004
Physics