6 papers
Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference
Hai-Ling Lu, Yu-Yang Li, Yin-Bi Li +4
Stellar spectra encode key information on the physical properties and chemical compositions of stars. Accurate stellar parameter determination is essential for addressing major que…
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…
Estimating stellar atmospheric parameters and elemental abundances using fully connected residual network
Shuo Li, Yin-Bi Li, A-Li Luo +3
Stellar atmospheric parameters and elemental abundances are traditionally determined using template matching techniques based on high-resolution spectra. However, these methods are…
Refined M-type Star Catalog from LAMOST DR10: Measurements of Radial Velocities, , log , [M/H] and [/M]
Shuo Li, Yin-Bi Li, A-Li Luo +10
Precise stellar parameters for M-type stars, the Galaxy's most common stellar type, are crucial for numerous studies. In this work, we refined the LAMOST DR10 M-type star catalog t…
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…