Early-type star param. from LAMOST LRS & MRS

Deriving the atmospheric parameters of massive stars is important to understanding their physical properties, and thus the atmospheric parameters are key inputs to trace the evolution of massive stars. Here we report our work on adopting the data-driven technique called stellar label machine (SLAM) with the nonlocal thermal equilibrium TLUSTY synthetic spectra as the training data set to estimate the stellar parameters of Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) optical spectra for early-type stars.

We apply two consistency tests to verify this machine-learning method and compare stellar labels given by SLAM with the labels in the literature for several objects having high-resolution spectra. We provide the stellar labels of effective temperature (Teff), surface gravity (logg), metallicity ([M/H]), and projected rotational velocity (vsini) for 3931 and 578 early-type stars from the LAMOST low-resolution survey (LRS) and medium-resolution survey (MRS), respectively.