Estimating Stellar Atmospheric Parameters and [{\alpha}/Fe] for LAMOST O-M type Stars Using a Spectral Emulator
Jun-chao Liang, A-Li Luo, Yin-Bi Li, Xiao-Xiao Ma, Shuo Li, Shu-Guo, Ma, Hai-Ling Lu, Yun-Jin Zhang, Bing Du, and Xiao Kong

TL;DR
This paper introduces a spectral emulator and grouping strategy to rapidly estimate stellar atmospheric parameters and [{}/Fe] for LAMOST O-M stars, improving parameter accuracy and catalog completeness.
Contribution
We developed a spectral emulator with grouping optimization to efficiently estimate stellar parameters for LAMOST spectra, addressing previous catalog limitations.
Findings
Effective parameter prediction within 70 hours on a single machine.
Internal error dispersions are within acceptable ranges for all parameters.
Identified deficiencies in official LAMOST and MaStar catalogs.
Abstract
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 estimate effective temperature (T_eff), surface gravity (log g), metallicity ([Fe/H]) and the abundance of alpha elements with respect to iron ([alpha/Fe]) for O-M-type stars within the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution spectra. The primary aim is to use a rapid spectral-fitting method, specifically the spectral emulator with the grouping optimization strategy, to create a comprehensive catalog for stars of all types within LAMOST, addressing the shortcomings in parameter estimations for both cold and hot stars present in the official LAMOST AFGKM-type catalog. This effort is part of our series of studies dedicated to establishing an empirical spectral library for…
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