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

Very metal-poor (VMP, [Fe/H]-2.0) from LAMOST DR8 for the experiments and apply random forest and support vector machine methods to make comparisons. The resolution of all spectra is reduced to $R\sim200$ to match the resolution of the CSST, followed by pre-processing and transformation into two-dimensional spectra for input into the CNN model. The validation and practicality of this model are also tested on the MARCS synthetic spectra. The results show that using the CNN model constructed in this paper, we obtain Mean Absolute Error (MAE) values of 99.40 K for $T_{\textrm{eff}}$ , 0.22 dex for $\log$ g, 0.14 dex for [Fe/H], and 0.26 dex for [C/Fe] on the test set. Besides, the CNN model can efficiently identify VMP stars with a precision rate of 94.77%, a recall rate of 93.73%, and an accuracy of 95.70%. This paper powerfully demonstrates the effectiveness of the proposed CNN model in estimating stellar parameters for low-resolution spectra ( $R\sim200$ ) and recognizing VMP stars that are of interest for stellar population and galactic evolution work.