12 citations · 19 across the 10 of their papers we have counts for
6 papers · 1 filter
Morphology classification for galaxies in the Kilo Degree Survey using a label-efficient self-supervised learning framework
Xu Huang, Rui Li, Liang Gao +14
Galaxy morphology classification is fundamental to understanding galaxy formation and evolution. The advent of large-scale sky surveys has produced an unprecedented volume of galax…
Using Deep Learning Methods to Detect for Ultra-diffuse Galaxies in KiDS
Hao Su, Rui Li, Nicola R. Napolitano +13
Ultra-diffuse Galaxies (UDGs) are a subset of Low Surface Brightness Galaxies (LSBGs), showing mean effective surface brightness fainter than and a d…
Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods
Aina Zhang, Xiaoming Kong, Bowen Liu +4
The formation and evolution of ring structures in galaxies are crucial for understanding the nature and distribution of dark matter, galactic interactions, and the internal secular…
Deblending Overlapping Galaxies in DECaLS Using Transformer-Based Algorithm: A Method Combining Multiple Bands and Data Types
Ran Zhang, Meng Liu, Zhenping Yi +8
In large-scale galaxy surveys, particularly deep ground-based photometric studies, galaxy blending is inevitable and poses a potential primary systematic uncertainty for upcoming s…
Low surface brightness galaxies from BASS+MzLS with Machine Learning
Peng-Liang Du, Wei Du, Bing-Qing Zhang +3
From 5000 deg of the combination of the Beijing-Arizona Sky Survey (BASS) and Mayall -band Legacy Survey (MzLS) which is also the northern sky region of the Dark En…
Edge-on Low-surface-brightness Galaxy Candidates Detected from SDSS Images Using YOLO
Yongguang Xing, Zhenping Yi, Zengxu Liang +7
Low-surface-brightness galaxies (LSBGs), fainter members of the galaxy population, are thought to be numerous. However, due to their low surface brightness, the search for a wide-a…