LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars
With the increasing amount of astronomical observation data, it is an inevitable trend to use artificial intelligence methods for automatic analysis and identification of light curves for full samples. However, data sets covering all known classes of variable stars that meet all research needs are n...
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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 |
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| Online Access: | https://doi.org/10.3847/1538-4365/ad785b |
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| author | Ya Fei Ce Yu Kun Li Xiaodian Chen Yajie Zhang Chenzhou Cui Jian Xiao Yunfei Xu Yihan Tao |
| author_facet | Ya Fei Ce Yu Kun Li Xiaodian Chen Yajie Zhang Chenzhou Cui Jian Xiao Yunfei Xu Yihan Tao |
| author_sort | Ya Fei |
| collection | DOAJ |
| description | With the increasing amount of astronomical observation data, it is an inevitable trend to use artificial intelligence methods for automatic analysis and identification of light curves for full samples. However, data sets covering all known classes of variable stars that meet all research needs are not yet available. There is still a lack of standard training data sets specifically designed for any type of light-curve classification, but existing light-curve training sets or data sets cannot be directly merged into a large collection. Based on the open data sets of the All-Sky Automated Survey for SuperNovae, Gaia, and Zwicky Transient Facility, we construct a compatible light-curve data set named LEAVES for automated recognition of variable stars, which can be used for training and testing new classification algorithms. The data set contains a total of 977,953 variable and 134,592 nonvariable light curves, in which the supported variables are divided into six superclasses and nine subclasses. We validate the compatibility of the data set through experiments and employ it to train a hierarchical random forest classifier, which achieves a weighted average F1-score of 0.95 for seven-class classification and 0.93 for 10-class classification. Experimental results prove that the classifier is more compatible than the classifier established based on a single band and a single survey, and has wider applicability while ensuring classification accuracy, which means it can be directly applied to different data types with only a relatively small loss in performance compared to a dedicated model. |
| format | Article |
| id | doaj-art-e85d18e06f52440fa39ed133dc3b11b6 |
| institution | OA Journals |
| issn | 0067-0049 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Supplement Series |
| spelling | doaj-art-e85d18e06f52440fa39ed133dc3b11b62025-08-20T02:19:07ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492024-01-0127511010.3847/1538-4365/ad785bLEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable StarsYa Fei0https://orcid.org/0009-0009-6603-416XCe Yu1https://orcid.org/0000-0003-2416-4547Kun Li2https://orcid.org/0000-0003-0324-0344Xiaodian Chen3https://orcid.org/0000-0001-7084-0484Yajie Zhang4https://orcid.org/0000-0003-2976-8198Chenzhou Cui5https://orcid.org/0000-0002-7456-1826Jian Xiao6https://orcid.org/0000-0003-0978-1280Yunfei Xu7https://orcid.org/0000-0002-7397-811XYihan Tao8https://orcid.org/0000-0002-3143-9337College of Intelligence and Computing, Tianjin University , No. 135 Yaguan Road, Haihe Education Park, Tianjin 300350, People's Republic of China ; yuce@tju.edu.cn; Technical R&D Innovation Center, National Astronomical Data Center , No. 135 Yaguan Road, Haihe Education Park, Tianjin 300350, People's Republic of ChinaCollege of Intelligence and Computing, Tianjin University , No. 135 Yaguan Road, Haihe Education Park, Tianjin 300350, People's Republic of China ; yuce@tju.edu.cn; Technical R&D Innovation Center, National Astronomical Data Center , No. 135 Yaguan Road, Haihe Education Park, Tianjin 300350, People's Republic of ChinaCollege of Intelligence and Computing, Tianjin University , No. 135 Yaguan Road, Haihe Education Park, Tianjin 300350, People's Republic of China ; yuce@tju.edu.cn; Technical R&D Innovation Center, National Astronomical Data Center , No. 135 Yaguan Road, Haihe Education Park, Tianjin 300350, People's Republic of ChinaNational Astronomical Observatories, Chinese Academy of Sciences , No. 20 Datun Road, Chaoyang District, Beijing 100012, People's Republic of ChinaCollege of Intelligence and Computing, Tianjin University , No. 135 Yaguan Road, Haihe Education Park, Tianjin 300350, People's Republic of China ; yuce@tju.edu.cn; Technical R&D Innovation Center, National Astronomical Data Center , No. 135 Yaguan Road, Haihe Education Park, Tianjin 300350, People's Republic of ChinaTechnical R&D Innovation Center, National Astronomical Data Center , No. 135 Yaguan Road, Haihe Education Park, Tianjin 300350, People's Republic of China; National Astronomical Observatories, Chinese Academy of Sciences , No. 20 Datun Road, Chaoyang District, Beijing 100012, People's Republic of ChinaCollege of Intelligence and Computing, Tianjin University , No. 135 Yaguan Road, Haihe Education Park, Tianjin 300350, People's Republic of China ; yuce@tju.edu.cn; Technical R&D Innovation Center, National Astronomical Data Center , No. 135 Yaguan Road, Haihe Education Park, Tianjin 300350, People's Republic of ChinaTechnical R&D Innovation Center, National Astronomical Data Center , No. 135 Yaguan Road, Haihe Education Park, Tianjin 300350, People's Republic of China; National Astronomical Observatories, Chinese Academy of Sciences , No. 20 Datun Road, Chaoyang District, Beijing 100012, People's Republic of ChinaTechnical R&D Innovation Center, National Astronomical Data Center , No. 135 Yaguan Road, Haihe Education Park, Tianjin 300350, People's Republic of China; National Astronomical Observatories, Chinese Academy of Sciences , No. 20 Datun Road, Chaoyang District, Beijing 100012, People's Republic of ChinaWith the increasing amount of astronomical observation data, it is an inevitable trend to use artificial intelligence methods for automatic analysis and identification of light curves for full samples. However, data sets covering all known classes of variable stars that meet all research needs are not yet available. There is still a lack of standard training data sets specifically designed for any type of light-curve classification, but existing light-curve training sets or data sets cannot be directly merged into a large collection. Based on the open data sets of the All-Sky Automated Survey for SuperNovae, Gaia, and Zwicky Transient Facility, we construct a compatible light-curve data set named LEAVES for automated recognition of variable stars, which can be used for training and testing new classification algorithms. The data set contains a total of 977,953 variable and 134,592 nonvariable light curves, in which the supported variables are divided into six superclasses and nine subclasses. We validate the compatibility of the data set through experiments and employ it to train a hierarchical random forest classifier, which achieves a weighted average F1-score of 0.95 for seven-class classification and 0.93 for 10-class classification. Experimental results prove that the classifier is more compatible than the classifier established based on a single band and a single survey, and has wider applicability while ensuring classification accuracy, which means it can be directly applied to different data types with only a relatively small loss in performance compared to a dedicated model.https://doi.org/10.3847/1538-4365/ad785bVariable starsLight curve classificationAstronomy databases |
| spellingShingle | Ya Fei Ce Yu Kun Li Xiaodian Chen Yajie Zhang Chenzhou Cui Jian Xiao Yunfei Xu Yihan Tao LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars The Astrophysical Journal Supplement Series Variable stars Light curve classification Astronomy databases |
| title | LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars |
| title_full | LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars |
| title_fullStr | LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars |
| title_full_unstemmed | LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars |
| title_short | LEAVES: An Expandable Light-curve Data Set for Automatic Classification of Variable Stars |
| title_sort | leaves an expandable light curve data set for automatic classification of variable stars |
| topic | Variable stars Light curve classification Astronomy databases |
| url | https://doi.org/10.3847/1538-4365/ad785b |
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