Radio Galaxy Classification

Radio galaxy classification aims to automatically categorize galaxies based on their radio emission morphology, a task crucial for understanding galaxy evolution and active galactic nuclei. Current research heavily utilizes machine learning, employing deep learning architectures like convolutional neural networks (CNNs), often incorporating group equivariance for rotation invariance and Bayesian methods for uncertainty quantification. These advancements are driven by the vast data volumes from modern radio telescopes and address the limitations of manual classification, ultimately improving our understanding of the universe and enabling more efficient analysis of astronomical data.