Galaxy Image

Galaxy image analysis focuses on extracting meaningful information from astronomical images to understand galaxy formation, evolution, and properties. Current research heavily utilizes machine learning, employing convolutional neural networks (CNNs), variational autoencoders (VAEs), diffusion models, and equivariant neural networks to classify galaxy morphologies, estimate redshifts, and deconvolute images to improve resolution. These advancements are crucial for efficiently analyzing the massive datasets from modern and upcoming surveys, enabling more accurate cosmological studies and a deeper understanding of the universe's structure. The development of robust, physics-informed models is a key focus, aiming to improve accuracy and reduce biases in automated analysis.