A Catalog of 12,766 Carbon-enhanced Metal-poor Stars from LAMOST Data Release 8

TL;DR

This paper presents a deep learning-based method to identify 12,766 Carbon-Enhanced Metal-Poor star candidates from LAMOST DR8 spectra, aiding studies of early universe and galaxy evolution.

Contribution

It introduces a novel deep learning scheme for detecting CEMP stars from low-resolution spectra and provides a large catalog of candidates with estimated stellar parameters.

Findings

Discovered 12,766 CEMP star candidates from LAMOST DR8 data.

Provided estimated stellar parameters for each candidate.

Demonstrated effectiveness of deep learning in spectral classification.

Abstract

Metal-poor stars are a rare and ancient type of stars; Carbon-Enhanced Metal-Poor (CEMP) stars are a subset of these celestial bodies that show an enrichment of carbon relative to iron. They are believed to be formed from gas polluted by the first generation of stars after the Big Bang and are important objects for studying the early universe, galaxy evolution, and nucleosynthesis. Due to their rarity, the search for metal-poor stars and CEMP stars is a valuable task. This study investigates the search for CEMP stars based on the low-resolution stellar spectra from LAMOST DR8, and proposes a deep learning scheme. From the LAMOST DR8 spectral library, this work discovered 12,766 CEMP star candidates. For ease of reference and use, we provide the estimated parameters $T_\texttt{eff}$, $\log~g$, [Fe/H] and [C/H] for them.