Galaxy Classification

Galaxy classification, the task of categorizing galaxies based on their properties, aims to understand galaxy formation and evolution. Current research heavily utilizes machine learning, particularly convolutional neural networks (CNNs) and graph neural networks (GNNs), often incorporating techniques like transfer learning, ensemble methods, and Bayesian approaches to improve accuracy and handle large datasets from surveys like the Sloan Digital Sky Survey (SDSS) and DESI. These advancements enable efficient analysis of vast astronomical data, facilitating more precise measurements of galaxy properties (e.g., mass, redshift, morphology) and improving cosmological models by incorporating detailed galaxy information. The resulting insights are crucial for advancing our understanding of the universe's structure and evolution.