Application of Support Vector Machines to the Classification of Galaxy Morphologies

Classifying galaxies into categories based on their structure has many practical applications in astronomy. In particular, large catalogues of classified galaxy images have been useful in many studies of the universe. However, one of the premier data sources in astronomy, the Sloan Digital Sky Survey (SDSS), does not provide classification information for the 50 million galaxy images it contains. As there are simply too many objects to classify manually, machine learning and classification algorithms are required to automate this process. This research applies the Support Vector Machine (SVM) algorithm to classify galaxy morphologies. The accuracy of the classification is measured on various categories of galaxies from the survey. A three class algorithm is presented that makes use of multiple SVMs. This algorithm is used to assign the categories of spiral, elliptical, and irregular galaxies.