A review of the search for AGB stars
The Asymptotic Giant Branch (AGB) is the late stage of the evolution of intermediate and low-mass stars and is of great importance for understanding stellar evolution, nucleosynthesis, and the chemical evolution of galaxies. This paper systematically reviews the methods for identifying AGB stars, fr...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Astronomy and Space Sciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fspas.2025.1587415/full |
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| author | Hai-Ling Lu Hai-Ling Lu Yin-Bi Li A-Li Luo A-Li Luo A-Li Luo Zhi-Qiang Zou Zhi-Qiang Zou Zhi-Qiang Zou Xiao-Ming Kong Zhen-Ping Yi Hugh R. A. Jones Jun-Chao Liang Jun-Chao Liang Shuo Li Shuo Li |
| author_facet | Hai-Ling Lu Hai-Ling Lu Yin-Bi Li A-Li Luo A-Li Luo A-Li Luo Zhi-Qiang Zou Zhi-Qiang Zou Zhi-Qiang Zou Xiao-Ming Kong Zhen-Ping Yi Hugh R. A. Jones Jun-Chao Liang Jun-Chao Liang Shuo Li Shuo Li |
| author_sort | Hai-Ling Lu |
| collection | DOAJ |
| description | The Asymptotic Giant Branch (AGB) is the late stage of the evolution of intermediate and low-mass stars and is of great importance for understanding stellar evolution, nucleosynthesis, and the chemical evolution of galaxies. This paper systematically reviews the methods for identifying AGB stars, from both traditional approaches and machine learning techniques. By integrating multi-wavelength data such as optical and infrared spectra, along with stellar evolution models, we analyze the existing methods and potential directions for improvement. We also explore the possibility of using interpretable machine learning algorithms to discover new features and applying deep learning algorithms to enhance search efficiency. With the advancement of data processing technology and the widespread application of machine learning methods, future AGB star searches will be more accurate and efficient. The increased number of discoveries, enabled by more advanced search methods, will particularly enhance our ability to reveal examples of short-lived late-stage stellar evolutionary processes. |
| format | Article |
| id | doaj-art-c9c41baa142844ea9ee7a96bcf188cc0 |
| institution | Kabale University |
| issn | 2296-987X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Astronomy and Space Sciences |
| spelling | doaj-art-c9c41baa142844ea9ee7a96bcf188cc02025-08-20T03:56:00ZengFrontiers Media S.A.Frontiers in Astronomy and Space Sciences2296-987X2025-07-011210.3389/fspas.2025.15874151587415A review of the search for AGB starsHai-Ling Lu0Hai-Ling Lu1Yin-Bi Li2A-Li Luo3A-Li Luo4A-Li Luo5Zhi-Qiang Zou6Zhi-Qiang Zou7Zhi-Qiang Zou8Xiao-Ming Kong9Zhen-Ping Yi10Hugh R. A. Jones11Jun-Chao Liang12Jun-Chao Liang13Shuo Li14Shuo Li15CAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, ChinaCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaSchool of Information Management and Institute for Astronomical Science, Dezhou University, Dezhou, ChinaCollege of Computer, Nanjing University of Posts and Telecommunications, Nanjing, ChinaJiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing, ChinaUniversity of Chinese Academy of Sciences, Nanjing, ChinaSchool of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, ChinaSchool of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, ChinaSchool of Physics, Astronomy and Mathematics, University of Hertfordshire, Hatfield, United KingdomCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaThe Asymptotic Giant Branch (AGB) is the late stage of the evolution of intermediate and low-mass stars and is of great importance for understanding stellar evolution, nucleosynthesis, and the chemical evolution of galaxies. This paper systematically reviews the methods for identifying AGB stars, from both traditional approaches and machine learning techniques. By integrating multi-wavelength data such as optical and infrared spectra, along with stellar evolution models, we analyze the existing methods and potential directions for improvement. We also explore the possibility of using interpretable machine learning algorithms to discover new features and applying deep learning algorithms to enhance search efficiency. With the advancement of data processing technology and the widespread application of machine learning methods, future AGB star searches will be more accurate and efficient. The increased number of discoveries, enabled by more advanced search methods, will particularly enhance our ability to reveal examples of short-lived late-stage stellar evolutionary processes.https://www.frontiersin.org/articles/10.3389/fspas.2025.1587415/fullasymptotic giant branch starslate-type starsstars evolutionHertzsprung–Russell and colour–magnitude diagramsmachine learning |
| spellingShingle | Hai-Ling Lu Hai-Ling Lu Yin-Bi Li A-Li Luo A-Li Luo A-Li Luo Zhi-Qiang Zou Zhi-Qiang Zou Zhi-Qiang Zou Xiao-Ming Kong Zhen-Ping Yi Hugh R. A. Jones Jun-Chao Liang Jun-Chao Liang Shuo Li Shuo Li A review of the search for AGB stars Frontiers in Astronomy and Space Sciences asymptotic giant branch stars late-type stars stars evolution Hertzsprung–Russell and colour–magnitude diagrams machine learning |
| title | A review of the search for AGB stars |
| title_full | A review of the search for AGB stars |
| title_fullStr | A review of the search for AGB stars |
| title_full_unstemmed | A review of the search for AGB stars |
| title_short | A review of the search for AGB stars |
| title_sort | review of the search for agb stars |
| topic | asymptotic giant branch stars late-type stars stars evolution Hertzsprung–Russell and colour–magnitude diagrams machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fspas.2025.1587415/full |
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