Fractal dimension analysis for automatic morphological galaxy classification

TL;DR

This study explores the use of fractal dimension analysis combined with machine learning algorithms to improve the automatic classification of galaxies into ellipticals, spirals, and irregulars, achieving high accuracy for some types.

Contribution

It introduces the application of Hausdorff-Besicovich fractal dimension as a feature for galaxy classification and demonstrates its effectiveness with machine learning methods.

Findings

Over 88% accuracy for ellipticals

100% accuracy for spirals

Over 40% accuracy for irregulars

Abstract

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.