AUTOMATED SOURCE CLASSIFICATION USING A KOHONEN NETWORK

We report progress in the development of an automatic star/galaxy classifier for processing images generated by large galaxy surveys like APM. Our classification method is based on neural networks using the Kohonen Self-organizing Map approach. Our method is novel, since it does not need supervised learning, i.e., the human factor, in training. The analysis presented here concentrates on separating point sources (stars) from extended ones. Using simple numerical experiments, we compare our method of image classification to the more traditional (PSF-fitting) approach of DAOFIND.