COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning
Building a comprehensive catalog of galaxy clusters is a fundamental task for studies on structure formation and galaxy evolution. In this paper, we present Cluster Optical Search using Machine Intelligence in Catalogs (COSMIC), an algorithm utilizing machine learning techniques to efficiently detec...
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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/ad8bbd |
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| author | Da-Chuan Tian Yang Yang Zhong-Lue Wen Jun-Qing Xia |
| author_facet | Da-Chuan Tian Yang Yang Zhong-Lue Wen Jun-Qing Xia |
| author_sort | Da-Chuan Tian |
| collection | DOAJ |
| description | Building a comprehensive catalog of galaxy clusters is a fundamental task for studies on structure formation and galaxy evolution. In this paper, we present Cluster Optical Search using Machine Intelligence in Catalogs (COSMIC), an algorithm utilizing machine learning techniques to efficiently detect galaxy clusters. COSMIC involves two steps, the identification of the brightest cluster galaxies and the estimation of cluster richness. We train our models on galaxy data from the Sloan Digital Sky Survey and the WHL galaxy cluster catalog. Validated against test data in the region of the northern Galactic cap, the COSMIC algorithm demonstrates high completeness when crossmatching with previous cluster catalogs. Richness comparison with previous optical and X-ray measurements also demonstrates a tight correlation. Our methodology showcases robust performance in galaxy cluster detection and holds promising prospects for applications in upcoming large-scale surveys. The COSMIC codes are published on https://github.com/tdccccc/COSMIC . |
| format | Article |
| id | doaj-art-5ecdf3f3d83d41388e69f08d1cba2117 |
| 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-5ecdf3f3d83d41388e69f08d1cba21172025-01-08T06:15:04ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492025-01-0127612110.3847/1538-4365/ad8bbdCOSMIC: A Galaxy Cluster–Finding Algorithm Using Machine LearningDa-Chuan Tian0https://orcid.org/0000-0002-4476-2520Yang Yang1Zhong-Lue Wen2https://orcid.org/0000-0001-9550-0929Jun-Qing Xia3Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University , Beijing 100875, People's Republic of China ; xiajq@bnu.edu.cn; School of Physics and Astronomy, Beijing Normal University , Beijing 100875, People's Republic of ChinaBeijing Navigation School , Beijing 101118, People's Republic of ChinaNational Astronomical Observatories, Chinese Academy of Sciences , 20A Datun Road, Chaoyang District, Beijing 100101, People's Republic of China ; zhonglue@nao.cas.cnInstitute for Frontiers in Astronomy and Astrophysics, Beijing Normal University , Beijing 100875, People's Republic of China ; xiajq@bnu.edu.cn; School of Physics and Astronomy, Beijing Normal University , Beijing 100875, People's Republic of ChinaBuilding a comprehensive catalog of galaxy clusters is a fundamental task for studies on structure formation and galaxy evolution. In this paper, we present Cluster Optical Search using Machine Intelligence in Catalogs (COSMIC), an algorithm utilizing machine learning techniques to efficiently detect galaxy clusters. COSMIC involves two steps, the identification of the brightest cluster galaxies and the estimation of cluster richness. We train our models on galaxy data from the Sloan Digital Sky Survey and the WHL galaxy cluster catalog. Validated against test data in the region of the northern Galactic cap, the COSMIC algorithm demonstrates high completeness when crossmatching with previous cluster catalogs. Richness comparison with previous optical and X-ray measurements also demonstrates a tight correlation. Our methodology showcases robust performance in galaxy cluster detection and holds promising prospects for applications in upcoming large-scale surveys. The COSMIC codes are published on https://github.com/tdccccc/COSMIC .https://doi.org/10.3847/1538-4365/ad8bbdGalaxy clustersBrightest cluster galaxiesClassificationConvolutional neural networks |
| spellingShingle | Da-Chuan Tian Yang Yang Zhong-Lue Wen Jun-Qing Xia COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning The Astrophysical Journal Supplement Series Galaxy clusters Brightest cluster galaxies Classification Convolutional neural networks |
| title | COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning |
| title_full | COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning |
| title_fullStr | COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning |
| title_full_unstemmed | COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning |
| title_short | COSMIC: A Galaxy Cluster–Finding Algorithm Using Machine Learning |
| title_sort | cosmic a galaxy cluster finding algorithm using machine learning |
| topic | Galaxy clusters Brightest cluster galaxies Classification Convolutional neural networks |
| url | https://doi.org/10.3847/1538-4365/ad8bbd |
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