Characteristics, Classification and Challenges in Searching for CEMP Stars

Abstract The study of carbon-enhanced metal-poor (CEMP) stars is of great significance for understanding the chemical evolution of the early universe and stellar formation. CEMP stars are characterized by carbon overabundance and are classified into several subclasses based on the abundance patterns of neutron-capture elements, including CEMP- s , CEMP-no, CEMP- r , and CEMP- r / s . These subclasses provide important insights into the formation of the first stars, early stellar nucleosynthesis, and supernova explosions. However, one of the major challenges in CEMP star research is the relatively small sample size of identified stars, which limits statistical analyses and hinders a comprehensive understanding of their properties. Fortunately, a series of large-scale spectroscopic survey projects have been launched and developed in recent years, providing unprecedented opportunities and technical challenges for the search and study of CEMP stars. To this end, this paper draws on the progress and future prospects of existing methods in constructing large CEMP data sets and offers an in-depth discussion from a technical standpoint, focusing on the strengths and limitations. In addition, we review recent advancements in the identification of CEMP stars, emphasizing the growing role of machine learning in processing and analyzing the increasingly large data sets generated by modern astronomical surveys. Compared to traditional spectral analysis methods, machine learning offers greater efficiency in handling complex data, automatic extraction of stellar parameters, and improved prediction accuracy. Despite these advancements, the research faces persistent challenges, including the scarcity of labeled samples, limitations imposed by low-resolution spectra, and the lack of interpretability in machine learning models. To address these issues, the paper proposes potential solutions and future research directions aimed at advancing the study of CEMP stars and enhancing our understanding of their role in the chemical evolution of the universe.

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Publication Details

Journal
Research in Astronomy and Astrophysics
Published
2025-04-15
DOI
https://doi.org/10.1088/1674-4527/adccf5
Primary Topic
Astronomical Observations and Instrumentation
Type
article
Field-Weighted Citation Impact
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article

Characteristics, Classification and Challenges in Searching for CEMP Stars

A-Li Luo, Xiaoming Kong, L. He, Xiangru Li et al.
Research in Astronomy and Astrophysics
Astronomical Observations and Instrumentation
article

Characteristics, Classification and Challenges in Searching for CEMP Stars

A-Li Luo, Xiaoming Kong, L. He, Xiangru Li, Yang Haifeng
article en

Abstract

Abstract The study of carbon-enhanced metal-poor (CEMP) stars is of great significance for understanding the chemical evolution of the early universe and stellar formation. CEMP stars are characterized by carbon overabundance and are classified into several subclasses based on the abundance patterns of neutron-capture elements, including CEMP- s , CEMP-no, CEMP- r , and CEMP- r / s . These subclasses provide important insights into the formation of the first stars, early stellar nucleosynthesis, and supernova explosions. However, one of the major challenges in CEMP star research is the relatively small sample size of identified stars, which limits statistical analyses and hinders a comprehensive understanding of their properties. Fortunately, a series of large-scale spectroscopic survey projects have been launched and developed in recent years, providing unprecedented opportunities and technical challenges for the search and study of CEMP stars. To this end, this paper draws on the progress and future prospects of existing methods in constructing large CEMP data sets and offers an in-depth discussion from a technical standpoint, focusing on the strengths and limitations. In addition, we review recent advancements in the identification of CEMP stars, emphasizing the growing role of machine learning in processing and analyzing the increasingly large data sets generated by modern astronomical surveys. Compared to traditional spectral analysis methods, machine learning offers greater efficiency in handling complex data, automatic extraction of stellar parameters, and improved prediction accuracy. Despite these advancements, the research faces persistent challenges, including the scarcity of labeled samples, limitations imposed by low-resolution spectra, and the lack of interpretability in machine learning models. To address these issues, the paper proposes potential solutions and future research directions aimed at advancing the study of CEMP stars and enhancing our understanding of their role in the chemical evolution of the universe.

Research in Astronomy and AstrophysicsVol. 25(5)
Shandong University (CN), South China Normal University (CN), Chinese Academy of Sciences (CN), National Astronomical Observatories (CN), University of Chinese Academy of Sciences (CN), Taiyuan University of Science and Technology (CN)
Openalex Percentile: Top 91%
Astronomical Observations and Instrumentation
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