An Updated Efficient Galaxy Morphology Classification Model Based on ConvNeXt Encoding with UMAP Dimensionality Reduction
Guanwen Fang, Shiwei Zhu, Jun Xu, Shiying Lu, Chichun Zhou, Yao Dai, Zesen Lin, Xu Kong · The Astronomical Journal · 2026
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 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.