The two-step galaxy morphology classification framework USmorph successfully combines unsupervised machine learning (UML) with supervised machine learning (SML) methods. To enhance the UML step, we employed a dual-encoder architecture (ConvNeXt and ViT) to effectively encode images, contrastive learning to accurately extract features, and principal component analysis to efficiently reduce dimensionality. Based on this improved framework, a sample of 46, 176 galaxies at 010⁹ M_⊙) to verify the validity of our galaxy morphological classification system. The analysis of morphological parameters is consistent with our classification system: SPH and ETD galaxies with higher n, G, and C tend to be more bulge-dominated and more compact compared with other types of galaxies. This demonstrates the reliability of this classification system, which will be useful for a forthcoming large-sky survey from the Chinese Space Station Telescope.
Yin et al. (Thu,) studied this question.