3 papers
astro-ph.GA2025
Robustness Analysis of USmorph: I. Generalization Efficiency of Unsupervised Strategies and Supervised Learning in Galaxy Morphological Classification
Shiwei Zhu, Guanwen Fang, Yao Dai +5
We conduct a systematic robustness analysis of the hybrid machine learning framework \texttt{USmorph}, which integrates unsupervised and supervised learning for galaxy morphologica…
astro-ph.GA2025
An updated efficient galaxy morphology classification model based on ConvNeXt encoding with UMAP dimensionality reduction
Guanwen Fang, Shiwei Zhu, Jun Xu +5
We present an enhanced unsupervised machine learning (UML) module within our previous \texttt{USmorph} classification framework featuring two components: (1) hierarchical feature e…
astro-ph.GA2025
Dual-coding contrastive learning based on ConvNeXt and ViT models for morphological classification of galaxies in COSMOS-Web
Shiwei Zhu, Guanwen Fang, Chichun Zhou +4
In our previous works, we proposed a machine learning framework named \texttt{USmorph} for efficiently classifying galaxy morphology. In this study, we propose a self-supervised me…