Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods
The formation and evolution of ring structures in galaxies are crucial for understanding the nature and distribution of dark matter, galactic interactions, and the internal secular evolution of galaxies. However, the limited number of existing ring galaxy catalogs has constrained deeper exploration...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal Supplement Series |
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| Online Access: | https://doi.org/10.3847/1538-4365/ade3c5 |
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| _version_ | 1849344143437856768 |
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| author | Aina Zhang Xiaoming Kong Bowen Liu Nan Li Yude Bu Zhenping Yi Meng Liu |
| author_facet | Aina Zhang Xiaoming Kong Bowen Liu Nan Li Yude Bu Zhenping Yi Meng Liu |
| author_sort | Aina Zhang |
| collection | DOAJ |
| description | The formation and evolution of ring structures in galaxies are crucial for understanding the nature and distribution of dark matter, galactic interactions, and the internal secular evolution of galaxies. However, the limited number of existing ring galaxy catalogs has constrained deeper exploration in this field. To address this gap, we introduce a two-stage binary classification model based on the Swin Transformer architecture to identify ring galaxies from the DESI Legacy Imaging Surveys. This model first selects potential candidates and then refines them in a second stage to improve classification accuracy. During model training, we investigated the impact of imbalanced data sets on the performance of the two-stage model. We experimented with various model combinations applied to the data sets of the DESI Legacy Imaging Surveys DR9, processing a total of 573,668 images with redshifts ranging from z_spec = 0.01–0.20 and mag _r < 17.5. After applying the two-stage filtering and conducting visual inspections, the overall precision of the models exceeded 64.87%, successfully identifying a total of 8052 newly discovered ring galaxies. With our catalog, the forthcoming spectroscopic data from DESI will facilitate a more comprehensive investigation into the formation and evolution of ring galaxies. |
| format | Article |
| id | doaj-art-aa487475652c480c8456a1fcc60d3bc3 |
| institution | Kabale University |
| issn | 0067-0049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Supplement Series |
| spelling | doaj-art-aa487475652c480c8456a1fcc60d3bc32025-08-20T03:42:44ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492025-01-0127925210.3847/1538-4365/ade3c5Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning MethodsAina Zhang0Xiaoming Kong1https://orcid.org/0000-0002-4764-4749Bowen Liu2Nan Li3Yude Bu4https://orcid.org/0000-0002-9474-4734Zhenping Yi5https://orcid.org/0000-0001-8590-4110Meng Liu6https://orcid.org/0000-0003-2442-2841School of Mechanical, Electrical & Information Engineering, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; xmkong@sdu.edu.cnSchool of Mechanical, Electrical & Information Engineering, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; xmkong@sdu.edu.cn; Shandong Key Laboratory of lntelligent Electronic Packaging Testing and Application, Shandong University , Weihai, 264209, Shandong, People’s Republic of ChinaSchool of Mechanical, Electrical & Information Engineering, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; xmkong@sdu.edu.cnNational Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China; School of Astronomy and Space Science, University of Chinese Academy of Science , Beijing 100049, People’s Republic of ChinaSchool of Mathematics and Statistics, Shandong University , Weihai, 264209, Shandong, People’s Republic of ChinaSchool of Mechanical, Electrical & Information Engineering, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; xmkong@sdu.edu.cn; Shandong Key Laboratory of lntelligent Electronic Packaging Testing and Application, Shandong University , Weihai, 264209, Shandong, People’s Republic of ChinaSchool of Mechanical, Electrical & Information Engineering, Shandong University , Weihai, 264209, Shandong, People’s Republic of China ; xmkong@sdu.edu.cn; Shandong Key Laboratory of lntelligent Electronic Packaging Testing and Application, Shandong University , Weihai, 264209, Shandong, People’s Republic of ChinaThe formation and evolution of ring structures in galaxies are crucial for understanding the nature and distribution of dark matter, galactic interactions, and the internal secular evolution of galaxies. However, the limited number of existing ring galaxy catalogs has constrained deeper exploration in this field. To address this gap, we introduce a two-stage binary classification model based on the Swin Transformer architecture to identify ring galaxies from the DESI Legacy Imaging Surveys. This model first selects potential candidates and then refines them in a second stage to improve classification accuracy. During model training, we investigated the impact of imbalanced data sets on the performance of the two-stage model. We experimented with various model combinations applied to the data sets of the DESI Legacy Imaging Surveys DR9, processing a total of 573,668 images with redshifts ranging from z_spec = 0.01–0.20 and mag _r < 17.5. After applying the two-stage filtering and conducting visual inspections, the overall precision of the models exceeded 64.87%, successfully identifying a total of 8052 newly discovered ring galaxies. With our catalog, the forthcoming spectroscopic data from DESI will facilitate a more comprehensive investigation into the formation and evolution of ring galaxies.https://doi.org/10.3847/1538-4365/ade3c5GalaxiesRing galaxiesGalaxy evolutionAstronomy data analysis |
| spellingShingle | Aina Zhang Xiaoming Kong Bowen Liu Nan Li Yude Bu Zhenping Yi Meng Liu Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods The Astrophysical Journal Supplement Series Galaxies Ring galaxies Galaxy evolution Astronomy data analysis |
| title | Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods |
| title_full | Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods |
| title_fullStr | Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods |
| title_full_unstemmed | Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods |
| title_short | Identifying Ring Galaxies in DESI Legacy Imaging Surveys Using Machine Learning Methods |
| title_sort | identifying ring galaxies in desi legacy imaging surveys using machine learning methods |
| topic | Galaxies Ring galaxies Galaxy evolution Astronomy data analysis |
| url | https://doi.org/10.3847/1538-4365/ade3c5 |
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