24 citations · 97 across the 20 of their papers we have counts for
11 papers · 1 filter
MSFA-Net: An Advanced Deep Learning Model for Identifying Blue Horizontal-Branch Stars from LAMOST DR12
Mingyuan Wang, Xiaoming Kong, Jie Ju +2
Blue horizontal-branch (BHB) stars are low-mass, core helium-burning objects with nearly constant luminosities, making them powerful tracers of old, metal-poor populations and valu…
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…
Deep learning-driven atmospheric parameter prediction for hot subdwarf stars with synthetic and observed spectra
Zhenxin Lei, Yangyang Dong, Bokai Kou +4
We design a convolutional neural network (CNN) incorporating channel attention and spatial attention mechanisms to predict atmospheric parameters of hot subdwarfs. The experimental…
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…
Estimating Stellar Parameters and Identifying Very Metal-poor Stars Using Convolutional Neural Networks for Low-resolution Spectra (R~200)
Tianmin Wu, Yude Bu, Jianhang Xie +5
Very metal-poor (VMP, [Fe/H]<-2.0) stars offer a wealth of information on the nature and evolution of elemental production in the early galaxy and universe. The upcoming China Spac…
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…
Identifying hot subdwarf stars from photometric data using Gaussian mixture model and graph neural network
Wei Liu, Yude Bu, Xiaoming Kong +2
Hot subdwarf stars are very important for understanding stellar evolution, stellar astrophysics, and binary star systems. Identifying more such stars can help us better understand…
Searching for Barium Stars from the LAMOST Spectra Using the Machine Learning Method: I
Fengyue Guo, Zhongding Cheng, Xiaoming Kong +5
Barium stars are chemically peculiar stars that exhibit enhancement of s-process elements. Chemical abundance analysis of barium stars can provide crucial clues for the study of th…
Determine the Masses and Ages of Red Giant Branch Stars from Low-resolution LAMOST Spectra Using DenseNet
Xuejie Li, Yude Bu, Jianhang Xie +2
We propose a new model to determine the ages and masses of red giant branch (RGB) stars from the low-resolution large sky area multi-object fiber spectroscopic telescope (LAMOST) s…
Searching for Hot Subdwarf Stars in LAMOST DR1-II. Pure spectroscopic identification method for hot subdwarfs
Zhenxin Lei, Yude Bu, Jingkun Zhao +2
Employing a new machine learning method, named hierarchical extreme learning machine (HELM) algorithm, we identified 56 hot subdwarf stars in the first data release (DR1) of the La…