34 citations · 45 across the 8 of their papers we have counts for
8 papers
A robust morphological classification method for galaxies using dual-encoding contrastive learning and multi-clustering voting on JWST/NIRCam images
Xiaolei Yin, Guanwen Fang, Shiying Lu +3
The two-step galaxy morphology classification framework {\tt USmorph} successfully combines unsupervised machine learning (UML) with supervised machine learning (SML) methods. To e…
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…
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…
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…
An efficient unsupervised classification model for galaxy morphology: Voting clustering based on coding from ConvNeXt large model
Guanwen Fang, Yao Dai, Zesen Lin +6
In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy mo…
Preparation for CSST: Star-galaxy Classification using a Rotationally Invariant Supervised Machine Learning Method
Shiliang Zhang, Guanwen Fang, Jie Song +6
Most existing star-galaxy classifiers depend on the reduced information from catalogs, necessitating careful data processing and feature extraction. In this study, we employ a supe…
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…
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…