Xianqi Liu, Xiangru Li, Ziyu Fang

Metal-poor (MP) stars are important targets for investigating the chemical evolution of the early universe. Among them, Carbon-Enhanced Metal-Poor (CEMP) stars have attracted extensive attention due to their rarity and astrophysical significance. Owing to their low occurrence rate, the identification of MP stars and CEMP stars remains a task of considerable scientific value. In this study, we investigate the search for CEMP stars based on the low-resolution stellar spectra from LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope) DR11 and propose a deep-learning-based approach for this purpose. By analyzing the LAMOST DR11 spectral library, we identify 1,408 CEMP star candidates. For ease of reference and further use, we provide the estimated stellar parameters for these objects, including $T_\texttt{eff}$, $\log~g$, [Fe/H], and [C/H].