5 papers
PISP: Projected-Space Inference of Stellar Parameters
Jun-Chao Liang, Yin-Bi Li, A-Li Luo +12
To improve the accuracy and efficiency of high-dimensional stellar parameter inference in large spectroscopic datasets, we propose a projection-assisted parameter-inference framewo…
Scalable Stellar Parameter Inference Using Python-based LASP: From CPU Optimization to GPU Acceleration
Jun-Chao Liang, Yin-Bi Li, A-Li Luo +13
To enhance the efficiency, scalability, and cross-survey applicability of stellar parameter inference in large spectroscopic datasets, we present a modular, parallelized Python fra…
Systematic search for blue hyper-velocity stars from LAMOST survey
Yongkang Sun, Yang Huang, Jifeng Liu +6
Hypervelocity stars (HVSs) represent a unique class of objects capable of escaping the gravitational pull of the Milky Way due to extreme acceleration events, such as close encount…
Estimating Stellar Atmospheric Parameters and [α/Fe] for LAMOST O-M type Stars Using a Spectral Emulator
Jun-chao Liang, A-Li Luo, Yin-Bi Li +7
In this paper, we developed a spectral emulator based on the Mapping Nearby Galaxies at Apache Point Observatory Stellar Library (MaStar) and a grouping optimization strategy to es…
An Empirical Sample of Spectra of M-type Stars with Homogeneous Atmospheric-Parameter Labels
Bing Du, A-Li Luo, Song Wang +6
The discrepancies between theoretical and observed spectra, and the systematic differences between various spectroscopic parameter estimates, complicate the determination of atmosp…