Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies

arXiv:2405.02366v1 Announce Type: cross
Abstract: This work is focused on the morphological classification of galaxies following the Hubble sequence in which the different classes are arranged in a hierarchy. The proposed method, BCNN, is composed of two main modules. First, a convolutional neural network (CNN) is trained with images of the different classes of galaxies (image augmentation is carried out to balance some classes); the CNN outputs the probability for each class of the hierarchy, and its outputs/predictions feed the …

abstract arxiv astro-ph.ga astro-ph.im bayesian classification cnn convolutional convolutional neural network cs.lg hierarchical images modules network networks neural network type work