Convolutional Neural Network-Based Morphological Image Classification of Galaxies

Abstract

This study explores the application of Convolutional Neural Networks (CNNs) for the morphological classification of galaxies based on astronomical images. The aim is to leverage the deep learning capabilities of CNNs to automate and enhance the efficiency of galaxy morphology analysis. The dataset comprises a diverse set of galaxy images obtained from astronomical observations, spanning various morphological types. We implement a CNN architecture designed to capture hierarchical features in galaxy images, enabling the network to learn and discriminate between different morphological characteristics. The model is trained on labelled data, and its performance is evaluated using a separate validation dataset. The results demonstrate the effectiveness of the CNN in accurately classifying galaxies into distinct morphological categories.