# The Early-type Stars from LAMOST survey: Atmospheric parameters

**Authors:** YanJun Guo, Bo Zhang, Chao Liu, Jiao Li, JiangDan Li, LuQian Wang, ZhiCun Liu, YongHui Hou, ZhanWen Han, XueFei Chen  
**arXiv:** [2110.06246](https://arxiv.org/abs/2110.06246)  
**Date:** 2021-10-12  
**Summary page:** https://www.summarizepaper.com/en/paper/2110.06246v1/

## Key points

- Massive stars are important in astrophysical processes and understanding their physical properties is crucial for tracing their evolution.
- Researchers used the data-driven technique Stellar Label Machine (SLAM) with non-LTE TLUSTY synthetic spectra as a training dataset to estimate atmospheric parameters of early-type stars observed by LAMOST.
- Two consistency tests were applied to verify the accuracy of the machine learning method, and SLAM's results were compared with literature values for several objects having high-resolution spectra.
- The study provides stellar labels for effective temperature ($T_\mathrm{eff}$), surface gravity ($\log{g}$), metallicity ([M/H]), and projected rotational velocity ($v\sin{i}$) for 3,931 and 578 early-type stars from LAMOST Low-Resolution Survey (LRS) and Medium-Resolution Survey (MRS), respectively.
- The uncertainties of $T_\mathrm{eff}$, $\log{g}$, and $v\sin{i}$ are $\sigma(T_\mathrm{eff}) = 2185$ K, $\sigma(\log{g}) = 0.29$ dex, and $\sigma(v\sin{i}) = 11 \,\rm km\,s^{-1}$ for MRS observations. For LRS spectra, these uncertainties are $\sigma(T_\mathrm{eff}) = 1642$ K, $\sigma(\log{g}) = 0.25$ dex, and $\sigma(v\sin{i}) = 42 \,\rm km\,s^{-1}$.
- LRS spectra better constrain parameters of $T_\mathrm{eff}$, $\log{g}$, and [M/H] due to their broad wavelength coverage. On the other hand, MRS spectra provide more accurate line profiles and thus better constrain $v \sin i$.
- This study demonstrates the effectiveness of using SLAM with non-LTE TLUSTY synthetic spectra as a training dataset to estimate atmospheric parameters for early type stars observed by LAMOST.
- The results provide valuable inputs for tracing the evolution of massive stars and understanding their physical properties.

## Summary

Massive stars play a crucial role in various astrophysical processes and understanding their physical properties is essential to trace their evolution. In this study, the researchers adopted the data-driven technique Stellar Label Machine (SLAM) with non-LTE TLUSTY synthetic spectra as the training dataset to estimate the atmospheric parameters of early-type stars observed by the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). The team applied two consistency tests to verify the machine learning method's accuracy and compared SLAM's results with literature values for several objects having high-resolution spectra. The study provides stellar labels for effective temperature ($T_\mathrm{eff}$), surface gravity ($\log{g}$), metallicity ([M/H]), and projected rotational velocity ($v\sin{i}$) for 3,931 and 578 early-type stars from LAMOST Low-Resolution Survey (LRS) and Medium-Resolution Survey (MRS), respectively. To estimate the average statistical uncertainties of their results, they calculated the standard deviation between predicted stellar labels and pre-labeled published values from high-resolution spectra. The uncertainties of $T_\mathrm{eff}$, $\log{g}$, and $v\sin{i}$ are $\sigma(T_\mathrm{eff}) = 2185$ K, $\sigma(\log{g}) = 0.29$ dex, and $\sigma(v\sin{i}) = 11 \,\rm km\,s^{-1}$ for MRS observations. For LRS spectra, these uncertainties are $\sigma(T_\mathrm{eff}) = 1642$ K, $\sigma(\log{g}) = 0.25$ dex, and $\sigma(v\sin{i}) = 42 \,\rm km\,s^{-1}$. The researchers found that LRS spectra better constrain parameters of $T_\mathrm{eff}$, $\log{g}$, and [M/H] due to their broad wavelength coverage. On the other hand, MRS spectra provide more accurate line profiles and thus better constrain $v \sin i$. This study demonstrates the effectiveness of using SLAM with non-LTE TLUSTY synthetic spectra as a training dataset to estimate atmospheric parameters for early type stars observed by LAMOST. The results provide valuable inputs for tracing the evolution of massive stars and understanding their physical properties.