Estimating stellar parameters and identifying very metal-poor stars for low-resolution spectra (R ∼ 200)

Very metal-poor (VMP, [Fe/H]<-2.0) stars offer a wealth of information on the nature and evolution of elemental production in the early galaxy and universe. The upcoming China Space Station Telescope (CSST) will provide us with a large amount of spectroscopic data that may contain plenty of VMP stars, and thus it is crucial to determine the stellar atmospheric parameters (T-eff, logg, and [Fe/H]) for low-resolution spectra similar to the CSST spectra (R similar to 200). In this paper, a two-dimensional Convolutional Neural Network (CNN) model with three convolutional layers and two fully connected layers is constructed. The principal aim of this work is to measure the ability of this model to estimate stellar parameters on low-resolution (R similar to 200) spectra and to identify VMP stars so that we can better search for VMP stars in the spectra observed by CSST.We mainly use 10,008 observed spectra of VMP stars from LAMOST DR3, and 16,638 spectra of common stars ([Fe/H]>-2.0) from LAMOST DR8 for the experiment and make comparisons. All spectra are reduced to R similar to 200 to match the resolution of the CSST and are preprocessed and collapsed into two-dimensional spectra for input to the CNN model. The results show that the MAE values are 99.40 K for T-eff, 0.22 dex for logg, 0.14 dex for [Fe/H], and 0.26 dex for [C/Fe], respectively. Besides, the CNN model efficiently identifies VMP stars with a precision of 94.77%. The validation and practicality of this model are also tested on the MARCS synthetic spectra. This paper powerfully demonstrates the effectiveness of the proposed CNN model in estimating stellar parameters for low-resolution spectra (R similar to 200) and recognizing VMP stars that are of interest for stellar population and galactic evolution work.