108 citations · 640 across the 48 of their papers we have counts for
48 papers
Difference Between Half-mass Radius and Half-light Radius of Galaxies at 0.2 2.5 Revealed by JWST/NIRCam Data
Mingen Xin, Guanwen Fang, Jie Song +3
Using JWST observations in CANDELS fields, we measure the half-light radius () and half-mass radius () for 14,333 galaxies with stellar masses $M_*…
Robustness Analysis of USmorph: II. Optimizing Feature Extraction, Dimensionality Reduction, and Clustering for Unsupervised Galaxy Morphology Classification
Guanwen Fang, Xiaolei Yin, Yirui Zheng +5
We conduct a systematic robustness analysis of the unsupervised machine learning module within the hybrid framework \texttt{USmorph}. This module automatically discovers morphologi…
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…
Transition from Outside-in to Inside-Out at : Evidence from Radial Profiles of Specific Star Formation Rate based on JWST/HST
Jie Song, Enci Wang, Cheng Jia +7
By combining high-resolution observations from JWST and HST, we have measured the stellar masses, star formation rates (SFRs), and multi-wavelength morphologies of galaxies in the…
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…
Multi-Layer Perceptron for Predicting Galaxy Parameters (MLP-GaP): stellar masses and star formation rates
Xiaotong Guo, Guanwen Fang, Haicheng Feng +1
The large-scale imaging survey will produce massive photometric data in multi-bands for billions of galaxies. Defining strategies to quickly and efficiently extract useful physical…
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…
USmorph: An Updated Framework of Automatic Classification of Galaxy Morphologies and Its Application to Galaxies in the COSMOS Field
Jie Song, GuanWen Fang, Shuo Ba +9
Morphological classification conveys abundant information on the formation, evolution, and environment of galaxies. In this work, we refine the two-step galaxy morphological classi…
Solution to the conflict between the resolved and unresolved galaxy stellar mass estimation from the perspective of JWST
Jie Song, GuanWen Fang, Zesen Lin +2
By utilizing the spatially-resolved photometry of galaxies at in the CEERS field, we estimate the resolved and unresolved stellar mass via spectral energy distribution…
Evolution of Non-parametric Morphology of Galaxies in the JWST CEERS Field at 0.8-3.0
Yao Yao, Jie Song, Xu Kong +3
Galaxy morphology is one of the most fundamental ways to describe galaxy properties, but the morphology we observe may be affected by wavelength and spatial resolution, which may i…
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…
A comparison of cosmological models with high-redshift quasars
Liuyuan Fan, Guanwen Fang, Jian Hu
The non-linear relationship between the monochromatic X-ray and UV luminosities in quasars offers the possibility of using high-z quasars as standard candles for cosmological testi…
The effect of environment on the properties of the most massive galaxies at in the cosmos-dash field
Jie Song, Guanwen Fang, Yizhou Gu +2
How the environment influences the most massive galaxies is still unclear. To explore the environmental effects on morphology and star formation in the most massive galaxies at hig…
Multiwavelength Analysis of a Nearby Heavily Obscured AGN in NGC 449
Xiaotong Guo, Qiusheng Gu, Jun Xu +5
We presented the multiwavelength analysis of a heavily obscured active galactic nucleus (AGN) in NGC 449. We first constructed a broadband X-ray spectrum using the latest NuSTAR an…
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…
The physical properties of massive green valley galaxies as a function of environments at in 3D-\textit{HST}/CANDELS fields
Wenjun Chang, Guanwen Fang, Yizhou Gu +3
To investigate the effects of environment in the quenching phase, we study the empirical relations for green valley (GV) galaxies between overdensity and other physical properties…
Automatic morphological classification of galaxies: convolutional autoencoder and bagging-based multiclustering model
C. C. Zhou, Y. Z. Gu, G. W. Fang +1
In order to obtain morphological information of unlabeled galaxies, we present an unsupervised machine-learning (UML) method for morphological classification of galaxies, which can…
AGN lifetimes in UV-selected galaxies: a clue to supermassive black hole-host galaxy coevolution
Xiaozhi Lin, Yongquan Xue, Guanwen Fang +3
The coevolution between supermassive black holes (SMBHs) and their host galaxies has been proposed for more than a decade, albeit with little direct evidence about black hole accre…
Morphological Transformation and Star Formation Quenching of Massive Galaxies at 0.5 < z < 2.5 in 3D-HST/CANDELS
Shuang Liu, Yizhou Gu, Qirong Yuan +4
To figure out the effect of stellar mass and local environment on morphological transformation and star formation quenching in galaxies, we use the massive ($M_* \geq 10^{10} M_{\o…
The effect of environment on star formation activity and morphology at 0.5 < z < 2.5 in CANDELS
Yizhou Gu, Guanwen Fang, Qirong Yuan +2
To explore the effect of environment on star-formation and morphological transformation of high-redshift galaxies, we present a robust estimation of localized galaxy overdensity us…
The effect of the morphological quenching mechanism on star formation activity at 0.5 < z < 1.5 in 3D-HST/CANDELS
Shiying Lu, Guanwen Fang, Yizhou Gu +3
Several mechanisms for the transformation of blue star-forming to red quiescent galaxies have been proposed, and the green valley (GV) galaxies amid them are widely accepted in a t…