Stellar parametrization of LAMOST M dwarf stars

TL;DR

This paper develops a machine learning-based catalog of stellar parameters for approximately 300,000 M dwarf stars from LAMOST and Gaia, achieving high accuracy in effective temperature and metallicity estimations.

Contribution

It introduces a novel application of the Stellar Label Machine (SLAM) to derive precise stellar parameters for a large M dwarf sample, utilizing both observational and atmospheric models.

Findings

SLAM achieves ~50K accuracy in $T_{eff}$ compared to APOGEE.

SLAM estimates [M/H] within 0.12 dex of APOGEE.

Model trained on atmospheric models has ~90K agreement.

Abstract

M dwarf stars are the most common stars in the Galaxy, dominating the population of the Galaxy by numbers at faint magnitudes. Precise and accurate stellar parameters for M dwarfs are of crucial importance for many studies. However, the atmospheric parameters of M dwarf stars are difficult to be determined. In this paper, we present a catalog of the spectroscopic stellar parameters ($T_{eff}$ and [M/H]) of $\sim$ 300,000 M dwarf stars observed by both LAMOST and Gaia using Stellar Label Machine (SLAM). We train a SLAM model using LAMOST spectra with APOGEE Data Release 16 (DR16) labels with $2800 \lt T_{eff} \lt 4500$K and $-2 \lt [M/H] \lt 0.5$ dex. The SLAM $T_{eff}$ is in agreement to within $\sim 50$K compared to the previous study determined by APOGEE observation, and SLAM [M/H] agree within 0.12 dex compared to the APOGEE observation. We also set up a SLAM model trained by BT-Settl atmospheric model, with random uncertainties (in cross-validation) to 60K and agree within $\sim 90$K compared to previous study.