DECISION TREES FOR AUTOMATED IDENTIFICATION OF COSMIC-RAY HITS IN HUBBLE-SPACE-TELESCOPE IMAGES

We have developed several algorithms for classifying objects in astronomical images. These algorithms have been used to label stars, galaxies, cosmic rays, plate defects, and other types of objects in sky surveys and other image databases. Our primary goal has been to develop techniques that classify with high accuracy, in order to ensure that celestial objects are not stored in the wrong catalogs. In addition, classification time must be fast due to the large number of classifications and to future needs for on-line classification systems. This paper reports on our results from using decision-tree classifiers to identify cosmic-ray hits in Hubble Space Telescope images. This method produces classifiers with over 95% accuracy using data from a single, unpaired image. Our experiments indicate that this accuracy will get even higher if methods for eliminating background noise improve.