An Empirical Sample of Spectra of M-type Stars with Homogeneous Atmospheric-parameter Labels

Abstract

The discrepancies between theoretical and observed spectra, and the systematic differences between various spectroscopic parameter estimates, complicate the determination of atmospheric parameters of M-type stars. In this work, we present an empirical sample of 5105 M-type star spectra with homogeneous atmospheric parameter labels through stellar-label transfer and sample cleaning. We addressed systematic discrepancies in spectroscopic parameter estimates by adopting recent results for Gaia EDR3 stars as a reference standard. Then, we used a density-based spatial clustering of applications with noise to remove unreliable samples in each subgrid of parameters. To confirm the reliability of the stellar labels, a five-layer neural network was utilized, randomly partitioning the samples into training and testing sets. The standard deviations between the predicted and actual values in the testing set are 14 K for T _eff, 0.06 dex for logg, and 0.05 dex for [M/H], respectively. In addition, we conducted an internal cross validation to enhance validation and obtained precisions of 11 K, 0.05 dex, and 0.05 dex for T _eff, logg, and [M/H], respectively. A grid of 1365 high-signal-to-noise ratio (S/N) spectra and their labels, selected from the empirical sample, was utilized in the stellar parameter pipeline for M-type stars of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), producing an almost seamless Kiel distribution diagram for LAMOST DR10 and DR11 data. The atmospheric parameters for M-type stars from LAMOST DR11 show improved precision compared to the data from DR9, with improvements (for spectra with S/N higher than 10) from 118 to 67 K in T _eff, 0.2 to 0.07 dex in logg, and 0.29 to 0.14 dex in [M/H].