3 papers
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.GA2023
The Classification of Galaxy Morphology in H-band of COSMOS-DASH Field: a combination-based machine learning clustering model
Yao Dai, Jun Xu, Jie Song +6
By applying our previously developed two-step scheme for galaxy morphology classification, we present a catalog of galaxy morphology for H-band selected massive galaxies in the COS…
astro-ph.GA2022
Automatic Classification of Galaxy Morphology: a rotationally invariant supervised machine learning method based on the UML-dataset
G. W. Fang, S. Ba, Y. Z. Gu +8
Classification of galaxy morphology is a challenging but meaningful task for the enormous amount of data produced by the next-generation telescope. By introducing the adaptive pola…