8 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…
A Gaia-linked High-purity QSO Candidate Catalog in Selected Fields with Extinction-binned Calibration and Spectrum-informed Training
Zi-Huang Cao, Zhao-Xiang Qi, Juan-Juan Ren +11
We present an extinction-calibrated, Gaia-source-level QSO candidate catalog for selected fields, designed as a high-purity input catalog for fiber-spectroscopic follow-up rather t…
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…
PhDLspec: physical-prior embedded deep learning method for spectroscopic determination of stellar labels in high-dimensional parameter space
Tianmin Wu, Maosheng Xiang, Jianrong Shi +4
Unlocking the full physical information encoded in low-resolution spectra poses a significant challenge for astronomical survey analysis. Such a task demands modeling spectra and o…
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…
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…