An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10
Blue horizontal-branch (BHB) stars are ideal tracers for studying the kinematics and structural properties of the Milky Way. With massive spectral data provided by the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), we aim to identify more potential BHB stars using machine learni...
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IOP Publishing
2025-01-01
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| Series: | The Astronomical Journal |
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| Online Access: | https://doi.org/10.3847/1538-3881/adea95 |
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| author | Yuhang Zhang Yude Bu Jiangchuan Zhang Ke Wang Huili Wu Mengmeng Zhang Shanshan Li Jingzhen Sun Xiaoming Kong Zhenping Yi Meng Liu |
| author_facet | Yuhang Zhang Yude Bu Jiangchuan Zhang Ke Wang Huili Wu Mengmeng Zhang Shanshan Li Jingzhen Sun Xiaoming Kong Zhenping Yi Meng Liu |
| author_sort | Yuhang Zhang |
| collection | DOAJ |
| description | Blue horizontal-branch (BHB) stars are ideal tracers for studying the kinematics and structural properties of the Milky Way. With massive spectral data provided by the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), we aim to identify more potential BHB stars using machine learning methods. In this study, we propose BHBNet, an advanced two-stage deep learning model integrating multiple techniques. By implementing Bayesian inference, it not only provides classification results but also quantifies uncertainty. In stage 1, a six-class classification model was constructed to initially identify BHB candidates, achieving a precision of 95.43% on the test set. In stage 2, a binary classification model constructed through the transfer learning method was employed to further refine the candidates, achieving a precision of 98.36% on the test set. Subsequently, by performing a two-stage search in LAMOST low-resolution survey DR10, we identified 6792 candidates. Nevertheless, since the completeness of this search result has not been assessed, these samples may not be adequate for statistical studies of the BHB population. We analyzed candidate properties including color, absolute magnitude, and spatial distribution, while estimating their atmospheric parameters. Eventually, by fitting Balmer line profiles, we identified 1605 new BHB stars compared to the previous studies by X.-X. Xue et al. and J. J. Vickers et al. Our study emphasizes the potential and effectiveness of using machine learning methods in identifying and analyzing BHB stars. |
| format | Article |
| id | doaj-art-390b422359b34cfc81dbded1e66d28e0 |
| institution | Kabale University |
| issn | 1538-3881 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astronomical Journal |
| spelling | doaj-art-390b422359b34cfc81dbded1e66d28e02025-08-20T03:41:05ZengIOP PublishingThe Astronomical Journal1538-38812025-01-01170315810.3847/1538-3881/adea95An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10Yuhang Zhang0https://orcid.org/0009-0009-7973-4077Yude Bu1https://orcid.org/0000-0002-9474-4734Jiangchuan Zhang2https://orcid.org/0009-0006-5523-3997Ke Wang3Huili Wu4Mengmeng Zhang5Shanshan Li6Jingzhen Sun7https://orcid.org/0009-0000-8724-497XXiaoming Kong8https://orcid.org/0000-0002-4764-4749Zhenping Yi9https://orcid.org/0000-0001-8590-4110Meng Liu10https://orcid.org/0000-0003-2442-2841School of Mathematics and Statistics, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; buyude@sdu.edu.cnSchool of Mathematics and Statistics, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; buyude@sdu.edu.cnSchool of Space Science and Technology, Shandong University , Weihai, 264209, Shandong, People’s Republic of ChinaSchool of Mathematics and Statistics, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; buyude@sdu.edu.cnSchool of Mathematics and Statistics, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; buyude@sdu.edu.cnSchool of Mathematics and Statistics, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; buyude@sdu.edu.cnSchool of Mathematics and Statistics, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; buyude@sdu.edu.cnSchool of Mathematics and Statistics, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; buyude@sdu.edu.cnSchool of Mechanical, Electrical and Information Engineering, Shandong University , Weihai, 264209, Shandong, People’s Republic of China; Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application, Shandong University , Weihai, 264209, Shandong, People’s Republic of ChinaSchool of Mechanical, Electrical and Information Engineering, Shandong University , Weihai, 264209, Shandong, People’s Republic of China; Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application, Shandong University , Weihai, 264209, Shandong, People’s Republic of ChinaSchool of Mechanical, Electrical and Information Engineering, Shandong University , Weihai, 264209, Shandong, People’s Republic of China; Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application, Shandong University , Weihai, 264209, Shandong, People’s Republic of ChinaBlue horizontal-branch (BHB) stars are ideal tracers for studying the kinematics and structural properties of the Milky Way. With massive spectral data provided by the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), we aim to identify more potential BHB stars using machine learning methods. In this study, we propose BHBNet, an advanced two-stage deep learning model integrating multiple techniques. By implementing Bayesian inference, it not only provides classification results but also quantifies uncertainty. In stage 1, a six-class classification model was constructed to initially identify BHB candidates, achieving a precision of 95.43% on the test set. In stage 2, a binary classification model constructed through the transfer learning method was employed to further refine the candidates, achieving a precision of 98.36% on the test set. Subsequently, by performing a two-stage search in LAMOST low-resolution survey DR10, we identified 6792 candidates. Nevertheless, since the completeness of this search result has not been assessed, these samples may not be adequate for statistical studies of the BHB population. We analyzed candidate properties including color, absolute magnitude, and spatial distribution, while estimating their atmospheric parameters. Eventually, by fitting Balmer line profiles, we identified 1605 new BHB stars compared to the previous studies by X.-X. Xue et al. and J. J. Vickers et al. Our study emphasizes the potential and effectiveness of using machine learning methods in identifying and analyzing BHB stars.https://doi.org/10.3847/1538-3881/adea95AstrostatisticsAstronomy data analysisHorizontal branch starsConvolutional neural networks |
| spellingShingle | Yuhang Zhang Yude Bu Jiangchuan Zhang Ke Wang Huili Wu Mengmeng Zhang Shanshan Li Jingzhen Sun Xiaoming Kong Zhenping Yi Meng Liu An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10 The Astronomical Journal Astrostatistics Astronomy data analysis Horizontal branch stars Convolutional neural networks |
| title | An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10 |
| title_full | An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10 |
| title_fullStr | An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10 |
| title_full_unstemmed | An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10 |
| title_short | An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10 |
| title_sort | advanced deep learning model for identifying blue horizontal branch stars from lamost dr10 |
| topic | Astrostatistics Astronomy data analysis Horizontal branch stars Convolutional neural networks |
| url | https://doi.org/10.3847/1538-3881/adea95 |
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