Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web
In our previous works, we proposed a machine learning framework named USmorph for efficiently classifying galaxy morphology. In this study, we propose a self-supervised method called contrastive learning to upgrade the unsupervised machine learning (UML) part of the USmorph framework, aiming to impr...
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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/add0b8 |
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| author | Shiwei Zhu Guanwen Fang Chichun Zhou Jie Song Zesen Lin Yao Dai Xu Kong |
| author_facet | Shiwei Zhu Guanwen Fang Chichun Zhou Jie Song Zesen Lin Yao Dai Xu Kong |
| author_sort | Shiwei Zhu |
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| description | In our previous works, we proposed a machine learning framework named USmorph for efficiently classifying galaxy morphology. In this study, we propose a self-supervised method called contrastive learning to upgrade the unsupervised machine learning (UML) part of the USmorph framework, aiming to improve the efficiency of feature extraction in this step. The upgraded UML method primarily consists of the following three aspects. (1) We employ a convolutional autoencoder to denoise galaxy images and adaptive polar coordinate transformation to enhance the model’s rotational invariance. (2) A pretrained dual-encoder convolutional neural network based on ConvNeXt and a vision transformer is used to encode the image data, while contrastive learning is then applied to reduce the dimension of the features. (3) We adopt a bagging-based clustering model to cluster galaxies with similar features into distinct groups. By carefully dividing the redshift bins, we apply this model to the rest-frame optical images of galaxies in the COSMOS-Web field within the redshift range of 0.5 < z < 6.0. Compared to the previous algorithm, the improved UML method successfully classifies 73% of galaxies. Using the GoogLeNet algorithm, we classify the morphology of the remaining 27% of galaxies. To validate the reliability of our updated algorithm, we compared our classification results with other galaxy morphological parameters and found a good consistency with galaxy evolution. Benefiting from its higher efficiency, this updated algorithm is well suited for application in future China Space Station Telescope missions. |
| format | Article |
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| institution | OA Journals |
| issn | 0067-0049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | The Astrophysical Journal Supplement Series |
| spelling | doaj-art-72af8f1eb7b44b11ba8cf59def4b2f2f2025-08-20T02:38:21ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492025-01-0127823910.3847/1538-4365/add0b8Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-WebShiwei Zhu0https://orcid.org/0009-0004-0966-6439Guanwen Fang1https://orcid.org/0000-0001-9694-2171Chichun Zhou2https://orcid.org/0000-0002-5133-2668Jie Song3https://orcid.org/0000-0002-0846-7591Zesen Lin4https://orcid.org/0000-0001-8078-3428Yao Dai5https://orcid.org/0000-0002-4638-0235Xu Kong6https://orcid.org/0000-0002-7660-2273School of Mathematics and Physics, Anqing Normal University , Anqing 246011, People’s Republic of China ; wen@mail.ustc.edu.cn; Institute of Astronomy and Astrophysics, Anqing Normal University , Anqing 246133, People’s Republic of ChinaSchool of Mathematics and Physics, Anqing Normal University , Anqing 246011, People’s Republic of China ; wen@mail.ustc.edu.cn; Institute of Astronomy and Astrophysics, Anqing Normal University , Anqing 246133, People’s Republic of ChinaSchool of Engineering, Dali University , Dali 671003, People’s Republic of ChinaDepartment of Astronomy, University of Science and Technology of China , Hefei 230026, People’s Republic of China ; xkong@ustc.edu.cn; School of Astronomy and Space Science, University of Science and Technology of China , Hefei 230026, People’s Republic of China; Institute of Deep Space Sciences , Deep Space Exploration Laboratory, Hefei 230026, People’s Republic of ChinaDepartment of Physics, The Chinese University of Hong Kong , Shatin, N.T., Hong Kong S.A.R., People’s Republic of ChinaShanghai Astronomical Observatory, Chinese Academy of Sciences , 80 Nandan Road, Shanghai 200030, People’s Republic of China; School of Astronomy and Space Science, University of Chinese Academy of Sciences , No. 19A Yuquan Road, Beijing 100049, People’s Republic of ChinaDepartment of Astronomy, University of Science and Technology of China , Hefei 230026, People’s Republic of China ; xkong@ustc.edu.cn; School of Astronomy and Space Science, University of Science and Technology of China , Hefei 230026, People’s Republic of China; Institute of Deep Space Sciences , Deep Space Exploration Laboratory, Hefei 230026, People’s Republic of ChinaIn our previous works, we proposed a machine learning framework named USmorph for efficiently classifying galaxy morphology. In this study, we propose a self-supervised method called contrastive learning to upgrade the unsupervised machine learning (UML) part of the USmorph framework, aiming to improve the efficiency of feature extraction in this step. The upgraded UML method primarily consists of the following three aspects. (1) We employ a convolutional autoencoder to denoise galaxy images and adaptive polar coordinate transformation to enhance the model’s rotational invariance. (2) A pretrained dual-encoder convolutional neural network based on ConvNeXt and a vision transformer is used to encode the image data, while contrastive learning is then applied to reduce the dimension of the features. (3) We adopt a bagging-based clustering model to cluster galaxies with similar features into distinct groups. By carefully dividing the redshift bins, we apply this model to the rest-frame optical images of galaxies in the COSMOS-Web field within the redshift range of 0.5 < z < 6.0. Compared to the previous algorithm, the improved UML method successfully classifies 73% of galaxies. Using the GoogLeNet algorithm, we classify the morphology of the remaining 27% of galaxies. To validate the reliability of our updated algorithm, we compared our classification results with other galaxy morphological parameters and found a good consistency with galaxy evolution. Benefiting from its higher efficiency, this updated algorithm is well suited for application in future China Space Station Telescope missions.https://doi.org/10.3847/1538-4365/add0b8Galaxy structureAstrostatistics techniquesAstronomy data analysis |
| spellingShingle | Shiwei Zhu Guanwen Fang Chichun Zhou Jie Song Zesen Lin Yao Dai Xu Kong Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web The Astrophysical Journal Supplement Series Galaxy structure Astrostatistics techniques Astronomy data analysis |
| title | Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web |
| title_full | Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web |
| title_fullStr | Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web |
| title_full_unstemmed | Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web |
| title_short | Dual-coding Contrastive Learning Based on the ConvNeXt and ViT Models for Morphological Classification of Galaxies in COSMOS-Web |
| title_sort | dual coding contrastive learning based on the convnext and vit models for morphological classification of galaxies in cosmos web |
| topic | Galaxy structure Astrostatistics techniques Astronomy data analysis |
| url | https://doi.org/10.3847/1538-4365/add0b8 |
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