The Early-type Stars from the LAMOST Survey: Atmospheric Parameters

Massive stars play key roles in many astrophysical processes. Deriving atmospheric parameters of massive is important to understand their physical properties and thus are inputs trace evolution. Here we report our work on adopting the data-driven technique Stellar LAbel Machine ({\tt SLAM}) with non-LTE TLUSTY synthetic spectra as training dataset estimate stellar LAMOST optical for early-type stars. We apply two consistency tests verify this machine learning method compare labels given by {\tt SLAM} that literature several objects having high-resolution spectra. provide effective temperature ($T_\mathrm{eff}$), surface gravity ($\log{g}$), metallicity ([M/H]), projected rotational velocity ($v\sin{i}$) 3,931 578 from Low-Resolution Survey (LAMOST-LRS) Medium-Resolution (LAMOST-MRS), respectively. To average statistical uncertainties results, calculated standard deviation between predicted label pre-labeled published values The four $\sigma(T_\mathrm{eff}) = 2,185 $K, $\sigma(\log{g}) 0.29$ dex, $\sigma(v\sin{i}) 11\, \rm km\,s^{-1}$ MRS, 1,642 0.25$ 42\, LRS spectra, notice $T_\mathrm{eff}$, $\log{g}$ [M/H] can be better constrained using rather than MRS most likely due broad wavelength coverage, while $v\sin{i}$ probably relatively accurate line profiles