15 citations · 57 across the 10 of their papers we have counts for
10 papers
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…
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…
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…