An Updated Efficient Galaxy Morphology Classification Model Based on ConvNeXt Encoding with UMAP Dimensionality Reduction

Abstract We present an enhanced unsupervised machine learning (UML) module within our previous USmorph classification framework featuring two components: (1) hierarchical feature extraction via a pretrained ConvNeXt convolutional neural network with transfer learning, and (2) nonlinear manifold learning using uniform manifold approximation and projection (UMAP) for topology-aware dimensionality reduction. This dual-stage design enables efficient knowledge transfer from large-scale visual datasets while preserving morphological pattern geometry through UMAP’s neighborhood preservation. We apply the upgraded UML on I -band images of 99,806 COSMOS galaxies at redshift 0.2 < z < 1.2 (to ensure rest-frame optical morphology) with I mag < 25. The predefined cluster number is optimized to 20 (reduced from 50 in the original framework), achieving significant computational savings. The 20 algorithmically identified clusters are merged into five physical morphology types. About 51% of galaxies (50,056) were successfully classified. To assess classification effectiveness, we tested morphological parameters for massive galaxies with M * > 10 9 M ⊙ . Our classification results align well with galaxy evolution theory. This improved algorithm significantly enhances galaxy morphology classification efficiency, making it suitable for large-scale sky surveys such as those planned with the China Space Station Telescope.