A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine Learning
Identifying and classifying variable stars is essential to time-domain astronomy. The Large Area Multi-Object Fiber Optic Spectroscopic Telescope (LAMOST) acquired a large amount of spectral data. However, there is no corresponding variable source-related information in the data, constraining LAMOST...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | The Astrophysical Journal Supplement Series |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4365/ad3452 |
| _version_ | 1797205835523293184 |
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| author | Peiyun Qiao Tingting Xu Feng Wang Ying Mei Hui Deng Lei Tan Chao Liu |
| author_facet | Peiyun Qiao Tingting Xu Feng Wang Ying Mei Hui Deng Lei Tan Chao Liu |
| author_sort | Peiyun Qiao |
| collection | DOAJ |
| description | Identifying and classifying variable stars is essential to time-domain astronomy. The Large Area Multi-Object Fiber Optic Spectroscopic Telescope (LAMOST) acquired a large amount of spectral data. However, there is no corresponding variable source-related information in the data, constraining LAMOST data utilization for scientific research. In this study, we systematically investigated variable source classification methods for LAMOST data. We constructed a 10-class classification model using three mainstream machine-learning methods. Through performance comparison, we chose the LightGBM and XGBoost models. We further identified variable source candidates in the r band in LAMOST DR9 and obtained 281,514 variable source candidates with probabilities greater than 95%. Subsequently, we filtered out the sources of periodic variable sources using the generalized Lomb–Scargle periodogram and classified these periodic variable sources using the classification model. Finally, we propose a reliable periodic variable star catalog containing 176,337 stars with specific types. |
| first_indexed | 2024-04-24T08:57:26Z |
| format | Article |
| id | doaj.art-52bbd1e64b79473d9257ccd3ece7d378 |
| institution | Directory Open Access Journal |
| issn | 0067-0049 |
| language | English |
| last_indexed | 2024-04-24T08:57:26Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Supplement Series |
| spelling | doaj.art-52bbd1e64b79473d9257ccd3ece7d3782024-04-16T05:58:49ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492024-01-012721110.3847/1538-4365/ad3452A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine LearningPeiyun Qiao0https://orcid.org/0009-0005-2544-0947Tingting Xu1https://orcid.org/0000-0002-9997-9524Feng Wang2https://orcid.org/0000-0002-9847-7805Ying Mei3https://orcid.org/0000-0002-7960-9251Hui Deng4https://orcid.org/0000-0002-8765-3906Lei Tan5https://orcid.org/0000-0001-6215-9242Chao Liu6https://orcid.org/0000-0002-1802-6917Center for Astrophysics and Great Bay Center of National Astronomical Data Center, Guangzhou University , Guangzhou 510006, People's Republic of China ; fengwang@gzhu.edu.cn, meiying@gzhu.edu.cn, denghui@gzhu.edu.cn; Peng Cheng Laboratory , Shenzhen, 518000, People's Republic of ChinaSchool of Mathematics and Computer Science, Yunnan Minzu University , Kunming, Yunnan, 650504, People’s Republic of ChinaCenter for Astrophysics and Great Bay Center of National Astronomical Data Center, Guangzhou University , Guangzhou 510006, People's Republic of China ; fengwang@gzhu.edu.cn, meiying@gzhu.edu.cn, denghui@gzhu.edu.cn; Peng Cheng Laboratory , Shenzhen, 518000, People's Republic of ChinaCenter for Astrophysics and Great Bay Center of National Astronomical Data Center, Guangzhou University , Guangzhou 510006, People's Republic of China ; fengwang@gzhu.edu.cn, meiying@gzhu.edu.cn, denghui@gzhu.edu.cn; Peng Cheng Laboratory , Shenzhen, 518000, People's Republic of ChinaCenter for Astrophysics and Great Bay Center of National Astronomical Data Center, Guangzhou University , Guangzhou 510006, People's Republic of China ; fengwang@gzhu.edu.cn, meiying@gzhu.edu.cn, denghui@gzhu.edu.cn; Peng Cheng Laboratory , Shenzhen, 518000, People's Republic of ChinaCenter for Astrophysics and Great Bay Center of National Astronomical Data Center, Guangzhou University , Guangzhou 510006, People's Republic of China ; fengwang@gzhu.edu.cn, meiying@gzhu.edu.cn, denghui@gzhu.edu.cn; Peng Cheng Laboratory , Shenzhen, 518000, People's Republic of ChinaUniversity of Chinese Academy of Sciences , Beijing, 100049, People's Republic of China; Key Laboratory of Space Astronomy and Technology, National Astronomical Observatories, Chinese Academy of Sciences , Beijing, 100101, People's Republic of ChinaIdentifying and classifying variable stars is essential to time-domain astronomy. The Large Area Multi-Object Fiber Optic Spectroscopic Telescope (LAMOST) acquired a large amount of spectral data. However, there is no corresponding variable source-related information in the data, constraining LAMOST data utilization for scientific research. In this study, we systematically investigated variable source classification methods for LAMOST data. We constructed a 10-class classification model using three mainstream machine-learning methods. Through performance comparison, we chose the LightGBM and XGBoost models. We further identified variable source candidates in the r band in LAMOST DR9 and obtained 281,514 variable source candidates with probabilities greater than 95%. Subsequently, we filtered out the sources of periodic variable sources using the generalized Lomb–Scargle periodogram and classified these periodic variable sources using the classification model. Finally, we propose a reliable periodic variable star catalog containing 176,337 stars with specific types.https://doi.org/10.3847/1538-4365/ad3452CatalogsVariable starsCross-validationLight curves |
| spellingShingle | Peiyun Qiao Tingting Xu Feng Wang Ying Mei Hui Deng Lei Tan Chao Liu A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine Learning The Astrophysical Journal Supplement Series Catalogs Variable stars Cross-validation Light curves |
| title | A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine Learning |
| title_full | A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine Learning |
| title_fullStr | A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine Learning |
| title_full_unstemmed | A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine Learning |
| title_short | A Classification Catalog of Periodic Variable Stars for LAMOST DR9 Based on Machine Learning |
| title_sort | classification catalog of periodic variable stars for lamost dr9 based on machine learning |
| topic | Catalogs Variable stars Cross-validation Light curves |
| url | https://doi.org/10.3847/1538-4365/ad3452 |
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