Galaxy Morphology Classification Using Deep Learning

Abstract

Galaxy morphology is the study of the shape and visual appearance of galaxies, such as spiral, smooth, edge-on, and other morphological types. Morphological classification plays an important role in understanding how galaxies form and evolve over cosmic time. Most existing machine learning approaches for galaxy morphology classification rely solely on RGB galaxy images, which primarily capture spatial information and lack the physical spectral context of galaxies. In contrast, astronomical spectral datacubes contain rich information across multiple wavelengths, providing insights into the internal and physical properties of galaxies. However, such spectral observations are available for only a limited number of objects. Motivated by the availability of spectral information during training, this work investigates whether spectral knowledge can be used to improve morphology classification when only RGB images are available during testing. Different embedding techniques, including Siamese Networks, Supervised Autoencoders, and channel-based architectures, are explored to learn meaningful latent representations from spectral datacubes. These embeddings are then integrated with corresponding RGB galaxy images using multimodal deep learning frameworks for effective feature learning and classification. Experimental results demonstrate that incorporating spectral embeddings during training can guide the learning process and improve galaxy morphology classification performance using image-only inputs at inference time.