12 citations · 16 across the 7 of their papers we have counts for
7 papers
A Generalist Model Including Evolved Star Mass and Age
Mengmeng Zhang, Yude Bu, Siqi Wang +10
Determining precise stellar ages and masses for evolved giants is crucial for Galactic archaeology but challenged by spectral degeneracies. Gaia's low-resolution XP spectra offer a…
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…
An adaptive parameter estimator for poor-quality spectral data of white dwarfs
Duo Xie, Jiangchuan Zhang, Yude Bu +3
White dwarfs represent the end stage for 97% of stars, making precise parameter measurement crucial for understanding stellar evolution. Traditional estimation methods involve fitt…
SPT: Spectral Transformer for Red Giant Stars Age and Mass Estimation
Mengmeng Zhang, Fan Wu, Yude Bu +4
The age and mass of red giants are essential for understanding the structure and evolution of the Milky Way. Traditional isochrone methods for these estimations are inherently limi…
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…
Se-ResNet+SVM model: an effective method of searching for hot subdwarfs from LAMOST
Cheng Zhongding, Kong xiaoming, Wu Tianmin +5
In this paper, we apply the feature-integration idea to fuse the abstract features extracted by Se-ResNet with experience features into hybrid features and input the hybrid feature…