Bayesian and convolutional networks for hierarchical morphological classification of galaxies

Abstract

In the universe, there are up to 2 trillion galaxies with different features ranging from the number of stars, light spectrum, age, or visual appearance. Consequently, automatic classifiers are required to perform this task; furthermore, as shown by some related works, while greater the number of classes considered, the performance of the classifiers tends to decrease. This work is focused on the morphological classification of galaxies. They can be associated with a subset of 10 classes arranged in a hierarchy derived from the Hubble sequence. The proposed method, Bayesian and Convolutional Neural Networks (BCNN), is composed of two main modules. The first module is a convolutional neural network trained with the images of galaxies, and its predictions feed the second module. The second module is a Bayesian network that evaluates the hierarchy and helps to improve the prediction accuracy by combining the predictions of the first module through probabilistic inference over the Bayesian network. A collection of galaxies sourced from the Principal Galaxies Catalog and the APM Equatorial Catalogue of Galaxies are used to perform the experiments. The results show that BCNN performed better than five CNNs in multiple evaluation measures, reaching the scores 83% in hierarchical F-measure, 78% in accuracy, and 67% in exact match evaluation.