Resolution Classifier

A new automated astronomical image classifier is described. The classifier is of the Bayesian type using maximum likelihood template fitting with Poisson noise. The method's advantages are that there is no need for an explicit galaxy model, it provides a continuous spectrum between totally unresolved objects and obviously diffuse resolved galaxies, and it mn assign a probability to the classification. The continuous nature of the classifier allows identification of intermediate types such as stellar objects with faint nebulosity and galaxies with bright unresolved nuclei. The ability to as sign a probability to each classification allows a determination of when the noise, plate quality, and scale of the images no longer gives a sensible division of stars and galaxies. Also the probability allows the weighting of objects in statistical studies relying on this separation, The method is applied to the catalog of 4 -meter prime focus plates automatically reduced by the FOCAS system It is compared with the hyperserfaoe clustering classifier of Jarvis and Tyson.