Morphological Classification of Galaxies using Computer Vision and Artificial Neural Networks: A Computational Scheme

Abstract

The morphology of galaxies is an important issue in the large scale study of the Universe. The Hubble Deep Field project has already shown that the Universe contains billions and billions of galaxies. The Sloan Digital Sky Survey is expected to map the sky for one million galaxies. One of the major challenges facing astronomers today is how to automatically identify and classify large number of galaxies that will began to show up in the hundreds of thousands of digitized images from sky surveys. Today it is possible to address this problem with the help of advances occurring in computer vision and artificial neural networks technology. This paper describes a computational scheme to develop an automatic galaxy classifier. From the scheme it is possible to visualize several different types of automatic galaxy classifiers. Two types are presented here with prototype models. The first type uses the geometric shape features as the basis for classification. The second uses the direct pixel images of galaxies and artificial neural networks to do the classification. The results show that geometric shape features are very good indicators of different types of nearby galaxies. Three test cases were presented to the prototype geometric shape classifier and it was able to successfully classify all three of them. The direct image based neural network classifier was able to learn 97% of the 171 training patterns presented to it. However when the network was presented a test set of 37 independent patterns, it was only able to classify 57% percent of the test cases. This study demonstrates that a very robust and efficient automated galaxy classifier based on shape features and artificial neural network can be develop.