Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation
Since the variety of their light curve morphologies, the vast majority of the known heartbeat stars (HBSs) have been discovered by manual inspection. Machine learning, which has already been successfully applied to the classification of variable stars based on light curves, offers another possibilit...
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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/aded86 |
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| author | Min-Yu Li Sheng-Bang Qian Li-Ying Zhu Wen-Ping Liao Lin-Feng Chang Er-Gang Zhao Xiang-Dong Shi Fu-Xing Li Qi-Bin Sun Ping Li |
| author_facet | Min-Yu Li Sheng-Bang Qian Li-Ying Zhu Wen-Ping Liao Lin-Feng Chang Er-Gang Zhao Xiang-Dong Shi Fu-Xing Li Qi-Bin Sun Ping Li |
| author_sort | Min-Yu Li |
| collection | DOAJ |
| description | Since the variety of their light curve morphologies, the vast majority of the known heartbeat stars (HBSs) have been discovered by manual inspection. Machine learning, which has already been successfully applied to the classification of variable stars based on light curves, offers another possibility for the automatic detection of HBSs. We propose a novel feature extraction approach for HBSs. First, the orbital frequencies are calculated automatically according to the Fourier spectra of the light curves. Then, the amplitudes of the first 100 harmonics are extracted. Finally, these harmonics are normalized as feature vectors of the light curve. A training data set of synthetic light curves is constructed using ELLC, and their features are fed into recurrent neural networks (RNNs) for supervised learning, with the expected output being the eccentricity of these light curves. The performance of the RNNs is evaluated using a test data set of synthetic light curves, achieving 95% accuracy. When applied to known HBSs from the Optical Gravitational Lensing Experiment, Kepler, and Transiting Exoplanet Survey Satellite surveys, the networks achieve an average accuracy of 86%. This method successfully identifies four new HBSs within the eclipsing binary catalog of Kirk et al. The use of orbital harmonics as features for HBSs proves to be a practical approach that significantly reduces the computational cost of neural networks. RNNs show excellent performance in recognizing this type of time series data. This method not only allows efficient identification of HBSs but can also be extended to recognize other types of periodic variable stars. |
| format | Article |
| id | doaj-art-5e7af91ab68048b8b7612ffa15bb72c5 |
| 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-5e7af91ab68048b8b7612ffa15bb72c52025-08-20T03:36:31ZengIOP PublishingThe Astronomical Journal1538-38812025-01-01170316110.3847/1538-3881/aded86Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and ValidationMin-Yu Li0https://orcid.org/0000-0002-8564-8193Sheng-Bang Qian1https://orcid.org/0000-0002-5995-0794Li-Ying Zhu2https://orcid.org/0000-0002-0796-7009Wen-Ping Liao3https://orcid.org/0000-0001-9346-9876Lin-Feng Chang4https://orcid.org/0000-0002-8421-4561Er-Gang Zhao5Xiang-Dong Shi6https://orcid.org/0000-0002-5038-5952Fu-Xing Li7https://orcid.org/0000-0002-0285-6051Qi-Bin Sun8https://orcid.org/0000-0003-0516-404XPing Li9https://orcid.org/0009-0004-0289-2732Yunnan Observatories, Chinese Academy of Sciences , Kunming 650216, People’s Republic of China ; zhuly@ynao.ac.cnDepartment of Astronomy, School of Physics and Astronomy, Key Laboratory of Astroparticle Physics of Yunnan Province, Yunnan University , Kunming 650091, People’s Republic of China ; qiansb@ynu.edu.cnYunnan Observatories, Chinese Academy of Sciences , Kunming 650216, People’s Republic of China ; zhuly@ynao.ac.cn; University of Chinese Academy of Sciences , No.1 Yanqihu East Road, Huairou District, Beijing 101408, People’s Republic of ChinaYunnan Observatories, Chinese Academy of Sciences , Kunming 650216, People’s Republic of China ; zhuly@ynao.ac.cn; University of Chinese Academy of Sciences , No.1 Yanqihu East Road, Huairou District, Beijing 101408, People’s Republic of ChinaDepartment of Astronomy, School of Physics and Astronomy, Key Laboratory of Astroparticle Physics of Yunnan Province, Yunnan University , Kunming 650091, People’s Republic of China ; qiansb@ynu.edu.cnYunnan Observatories, Chinese Academy of Sciences , Kunming 650216, People’s Republic of China ; zhuly@ynao.ac.cnYunnan Observatories, Chinese Academy of Sciences , Kunming 650216, People’s Republic of China ; zhuly@ynao.ac.cnDepartment of Astronomy, School of Physics and Astronomy, Key Laboratory of Astroparticle Physics of Yunnan Province, Yunnan University , Kunming 650091, People’s Republic of China ; qiansb@ynu.edu.cnDepartment of Astronomy, School of Physics and Astronomy, Key Laboratory of Astroparticle Physics of Yunnan Province, Yunnan University , Kunming 650091, People’s Republic of China ; qiansb@ynu.edu.cnYunnan Observatories, Chinese Academy of Sciences , Kunming 650216, People’s Republic of China ; zhuly@ynao.ac.cn; University of Chinese Academy of Sciences , No.1 Yanqihu East Road, Huairou District, Beijing 101408, People’s Republic of ChinaSince the variety of their light curve morphologies, the vast majority of the known heartbeat stars (HBSs) have been discovered by manual inspection. Machine learning, which has already been successfully applied to the classification of variable stars based on light curves, offers another possibility for the automatic detection of HBSs. We propose a novel feature extraction approach for HBSs. First, the orbital frequencies are calculated automatically according to the Fourier spectra of the light curves. Then, the amplitudes of the first 100 harmonics are extracted. Finally, these harmonics are normalized as feature vectors of the light curve. A training data set of synthetic light curves is constructed using ELLC, and their features are fed into recurrent neural networks (RNNs) for supervised learning, with the expected output being the eccentricity of these light curves. The performance of the RNNs is evaluated using a test data set of synthetic light curves, achieving 95% accuracy. When applied to known HBSs from the Optical Gravitational Lensing Experiment, Kepler, and Transiting Exoplanet Survey Satellite surveys, the networks achieve an average accuracy of 86%. This method successfully identifies four new HBSs within the eclipsing binary catalog of Kirk et al. The use of orbital harmonics as features for HBSs proves to be a practical approach that significantly reduces the computational cost of neural networks. RNNs show excellent performance in recognizing this type of time series data. This method not only allows efficient identification of HBSs but can also be extended to recognize other types of periodic variable stars.https://doi.org/10.3847/1538-3881/aded86Binary starsElliptical orbitsStellar oscillationsNeural networks |
| spellingShingle | Min-Yu Li Sheng-Bang Qian Li-Ying Zhu Wen-Ping Liao Lin-Feng Chang Er-Gang Zhao Xiang-Dong Shi Fu-Xing Li Qi-Bin Sun Ping Li Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation The Astronomical Journal Binary stars Elliptical orbits Stellar oscillations Neural networks |
| title | Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation |
| title_full | Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation |
| title_fullStr | Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation |
| title_full_unstemmed | Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation |
| title_short | Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation |
| title_sort | heartbeat stars recognition based on recurrent neural networks method and validation |
| topic | Binary stars Elliptical orbits Stellar oscillations Neural networks |
| url | https://doi.org/10.3847/1538-3881/aded86 |
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