A Catalog of 1,408 Carbon-Enhanced Metal-Poor Stars from LAMOST DR11

TL;DR

This paper presents a deep-learning method to identify 1,408 carbon-enhanced metal-poor star candidates from LAMOST DR11 spectra, aiding studies of early universe chemical evolution.

Contribution

It introduces a novel deep-learning approach for detecting CEMP stars in low-resolution spectra and provides a catalog of candidates with stellar parameters.

Findings

Identified 1,408 CEMP star candidates from LAMOST DR11.

Provided estimated stellar parameters for each candidate.

Enhanced methods for studying early universe chemical evolution.

Abstract

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].