Refined M-type Star Catalog from LAMOST DR10: Measurements of Radial Velocities, $T_\text{eff}$, log $g$, [M/H] and [$\alpha$/M]

TL;DR

This study refines the LAMOST DR10 M-type star catalog by removing non-M spectra, correcting classifications, and applying a CNN-based label transfer to accurately measure stellar parameters, significantly improving precision over previous results.

Contribution

The paper introduces a two-stage process combining deep learning and label transfer with APOGEE data to enhance the accuracy and reliability of M-type star parameters in the LAMOST catalog.

Findings

Cleaned and expanded the M-type star catalog to 870,518 spectra.

Achieved over 20 ext{ }% improvement in parameter precision for M dwarfs.

Achieved over 50 ext{ }% improvement in parameter precision for M giants.

Abstract

Precise stellar parameters for M-type stars, the Galaxy's most common stellar type, are crucial for numerous studies. In this work, we refined the LAMOST DR10 M-type star catalog through a two-stage process. First, we purified the catalog using techniques including deep learning and color-magnitude diagrams to remove 22,496 non-M spectra, correct 2,078 dwarf/giant classifications, and update 12,900 radial velocities. This resulted in a cleaner catalog containing 870,518 M-type spectra (820,493 dwarfs, 50,025 giants). Second, applying a label transfer strategy using values from APOGEE DR16 for parameter prediction with a ten-fold cross-validated CNN ensemble architecture, we predicted $T_\text{eff}$, $\log g$, [M/H], and [$\alpha$/M] separately for M dwarfs and giants. The average internal errors for M dwarfs/giants are respectively: $T_\text{eff}$ 30/17 K, log $g$ 0.07/0.07 dex, [M/H] 0.07/0.05 dex, and [$\alpha$/M] 0.02/0.02 dex. Comparison with APOGEE demonstrates external precisions of 34/14 K, 0.12/0.07 dex, 0.09/0.04 dex, and 0.03/0.02 dex for M dwarfs/giants, which represents precision improvements of over 20\% for M dwarfs and over 50\% for M giants compared to previous literature results. The catalog is available at https://nadc.china-vo.org/res/r101668/.