Fractal dimension analysis for automatic morphological galaxy classification
This work addresses galaxy classification for astronomers, but it is incremental as it applies existing methods to a new dataset with some improvement.
The paper tackled morphological galaxy classification by using fractal dimension as a feature, achieving over 88% accuracy for elliptical galaxies, 100% for spirals, and over 40% for irregulars in preliminary experiments.
In this report we present experimental results using \emph{Haussdorf-Besicovich} fractal dimension for performing morphological galaxy classification. The fractal dimension is a topological, structural and spatial property that give us information about the space were an object lives. We have calculated the fractal dimension value of the main types of galaxies: ellipticals, spirals and irregulars; and we use it as a feature for classifying them. Also, we have performed an image analysis process in order to standardize the galaxy images, and we have used principal component analysis to obtain the main attributes in the images. Galaxy classification was performed using machine learning algorithms: C4.5, k-nearest neighbors, random forest and support vector machines. Preliminary experimental results using 10-fold cross-validation show that fractal dimension helps to improve classification, with over 88 per cent accuracy for elliptical galaxies, 100 per cent accuracy for spiral galaxies and over 40 per cent for irregular galaxies.