The Blue Horizontal-Branch Stars From the LAMOST Survey: Atmospheric Parameters
Jie Ju, Bo Zhang, Wenyuan Cui, ZhenYan Huo, Chao Liu, Yang Huang, and, JianRong Shi

TL;DR
This study uses a data-driven method called SLAM to accurately determine atmospheric parameters of 5,355 BHB stars from LAMOST spectra, improving robustness especially for low S/N data by incorporating color indexes.
Contribution
Introduces the stellar label machine (SLAM), a novel data-driven approach that enhances the accuracy of atmospheric parameter estimation for BHB stars using low-resolution spectra.
Findings
Improved precision of $T_{eff}$, log $g$, and [Fe/H] for BHB stars, especially at low S/N.
Achieved typical uncertainties of 30 K, 0.1 dex, and 0.12 dex for the parameters.
Validated results against high-resolution spectra, confirming the method's reliability.
Abstract
Blue horizontal-branch (BHB) stars are crucial for studying the structure of the Galactic halo. Accurate atmospheric parameters of BHB stars are essential for investigating the formation and evolution of the Galaxy. In this work, a data-driven technique named stellar label machine (SLAM) is used to estimate the atmospheric parameters of Large Sky Area Multi-Object Fiber Spectroscopic Telescope low-resolution spectra (LAMOST-LRS) for BHB stars with a set of A-type theoretical spectra as the training dataset. We add color indexes () during the training process to constrain the stellar temperature further. Finally, we derive the atmospheric parameters (, log\, , [Fe/H]) for 5,355 BHB stars. Compared to existing literature results, our results are more robust, after taking the color index into account, the resulted precisoin of…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Astronomical Observations and Instrumentation · Stellar, planetary, and galactic studies
