34 citations · 73 across the 21 of their papers we have counts for
21 papers
Searching for Type Ia Supernovae in the Dark Energy Spectroscopic Instrument
Xiaoyu Zhuang, Song Ran, Dezheng Meng +9
With the development of large-scale photometric surveys, an increasing number of supernova candidates are being discovered, leading to a rapidly growing demand for supernova spectr…
SDSS+JWST Census of Stellar and Nebular Dust Attenuation at -7: Mass Dependence and Redshift Evolution
Jie Song, Masami Ouchi, Tomokazu Kiyota +3
We present the demography of dust attenuation, including its mass dependence and redshift evolution, using spectroscopic samples of 34,182 SDSS galaxies at and 863 JWST/…
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…
Discovery of low-redshift analogues to "Little Red Dots" in DESI: A later evolutionary stage of compact LRDs?
Weiyu Ding, Xu Kong, Wei-Jian Guo +8
The James Webb Space Telescope (JWST) has recently discovered a population of compact, red sources at z > 4 known as "Little Red Dots" (LRDs). They are characterized by their V-sha…
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…
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…
First Statistical Detection of MgII-traced Cool Gas Outflows with JWST Towards Cosmic Dawn
Cheqiu Lyu, Haoran Yu, Enci Wang +9
Galactic-scale outflows are a crucial component of galaxy evolution, yet their properties in the early universe remain poorly constrained. We present the first statistical investig…
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…
Central Concentration and Escape of Ionizing Photons in Galaxies at the Epoch of Reionization
Cheqiu Lyu, Enci Wang, Junxian Wang +9
Compact, low-mass galaxies with strong nebular emission are considered promising candidates for efficient ionizing photon production and escape. We present a spatially resolved ana…
Potential-Driven Metal Cycling: JADES Census of Gas-Phase Metallicity for galaxies at 1 < z < 7
Cheng Jia, Enci Wang, Cheqiu Lyu +9
The gravitational potential is established as a critical determinant of gas-phase metallicity (12+log(O/H)) in low-redshift galaxies, whereas its influence remains unconfirmed at h…
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…
Size Growth on Short Timescales of Star-Forming Galaxies: Insights from Size Variation with Rest-Frame Wavelength with JADES
Cheng Jia, Enci Wang, Huiyuan Wang +11
We investigate size variation with rest-frame wavelength for star-forming galaxies based on the second JWST Advanced Deep Extragalactic Survey data release. Star-forming galaxies a…
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…
Strong [O III] λ5007 Compact Galaxies Identified from SDSS DR16 and Their Scaling Relations
Weiyu Ding, Hu Zou, Xu Kong +11
Green pea galaxies are a special class of star-forming compact galaxies with strong [O III]λ5007 and considered as analogs of high-redshift Lyα-emitting galaxies and potential sour…
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…
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…
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…