Galaxy Morphology

Galaxy morphology research focuses on classifying and understanding the shapes of galaxies, crucial for unraveling galaxy formation and evolution. Current efforts leverage machine learning, employing architectures like convolutional neural networks (CNNs), graph neural networks (GNNs), and transformers, often incorporating domain adaptation techniques to improve robustness across diverse datasets and handle noisy or incomplete data. These advancements enable efficient automated classification of vast astronomical surveys, improving the accuracy and speed of cosmological analyses and facilitating discoveries related to dark matter, dark energy, and galaxy evolution.