Estimating Stellar Atmospheric Parameters from the LAMOST DR6 Spectra with SCDD Model

Abstract

Accurate and efficient determination of stellar atmospheric parameters (T eff , logg, [Fe/H]) is essential for large-scale sky survey to conduct Galactic archeology and stellar evolution history. This paper proposes a novel data-driven model based on statistical features and Catboost algorithm (SCDD). The model extracts the statistical features of the spectra by windows, and constructs a nonlinear mapping based on the Catboost algorithm between the spectral features and the stellar parameters. After being trained using LAMOST DR6 spectral data set, the SCDD showed excellent results in the estimation of the stellar parameters. In the condition of that the g-band signal-to-noise ratio (S/Ng) is higher than 100, the root mean square errors (RMSEs) of T eff, logg, and [Fe/H] are 36 K, 0.077 dex and 0.037 dex, respectively. Compared with the StarNet and Cannon2 models, SCDD performs better in mean absolute error and RMSE, which proves its good fitting ability. In addition, this paper compares the parameters estimated by the SCDD with those of APOGEE. The results are in good agreement, which shows that the model we proposed is reliable.