NAOC Open IR
Open clusters identifying by multi-scale density feature learning
Xiang, Yaobing1,2,3; Xi, Jiangbo4; Shao, Zhengyi5,6; Wang, Min1,2,3; Yang, Yun4
2021-02-01
Source PublicationASTROPHYSICS AND SPACE SCIENCE
ISSN0004-640X
Volume366Issue:2Pages:11
AbstractOpen clusters (OCs) are important objects in exploring the structure and history of the Milky Way. Large amount of sky survey data can be used to detect OCs. However, analyzing these data manually has become a bottleneck of OC identification. This study proposes a multi-scale density feature learning (MSDFL), which includes the open cluster kernel density map to visualize the features of OCs; and open cluster identifying network, which is a deep learning model used to perform identifying with the maps. A test set and experimental region are utilized to evaluate the effectiveness of our method. For OCs that stand out as significant overdensities, experimental results show that the MSDFL method can achieve the accuracy of 94%. Lastly, the proposed method can successfully identify real OCs in the experimental sky region. The code is available at: https://gitee.com/colab_worker/cluster_search..
KeywordOpen cluster Multi-scale Kernel density estimation Machine learning
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Geo-Information Engineering ; State Key Laboratory of Geo-Information Engineering ; Fundamental Research Funds for the Central Universities, CHD ; Fundamental Research Funds for the Central Universities, CHD ; National Key R&D Program of China ; National Key R&D Program of China ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Geo-Information Engineering ; State Key Laboratory of Geo-Information Engineering ; Fundamental Research Funds for the Central Universities, CHD ; Fundamental Research Funds for the Central Universities, CHD ; National Key R&D Program of China ; National Key R&D Program of China ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Geo-Information Engineering ; State Key Laboratory of Geo-Information Engineering ; Fundamental Research Funds for the Central Universities, CHD ; Fundamental Research Funds for the Central Universities, CHD ; National Key R&D Program of China ; National Key R&D Program of China ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Geo-Information Engineering ; State Key Laboratory of Geo-Information Engineering ; Fundamental Research Funds for the Central Universities, CHD ; Fundamental Research Funds for the Central Universities, CHD ; National Key R&D Program of China ; National Key R&D Program of China ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory
DOI10.1007/s10509-021-03923-9
Language英语
Funding ProjectNational Natural Science Foundation of China[61806022] ; State Key Laboratory of Geo-Information Engineering[SKLGIE2018-M-3-4] ; Fundamental Research Funds for the Central Universities, CHD[300102269103] ; Fundamental Research Funds for the Central Universities, CHD[300102269304] ; Fundamental Research Funds for the Central Universities, CHD[300102269205] ; National Key R&D Program of China[2019YFA0405501] ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Geo-Information Engineering ; State Key Laboratory of Geo-Information Engineering ; Fundamental Research Funds for the Central Universities, CHD ; Fundamental Research Funds for the Central Universities, CHD ; National Key R&D Program of China ; National Key R&D Program of China ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Geo-Information Engineering ; State Key Laboratory of Geo-Information Engineering ; Fundamental Research Funds for the Central Universities, CHD ; Fundamental Research Funds for the Central Universities, CHD ; National Key R&D Program of China ; National Key R&D Program of China ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Geo-Information Engineering ; State Key Laboratory of Geo-Information Engineering ; Fundamental Research Funds for the Central Universities, CHD ; Fundamental Research Funds for the Central Universities, CHD ; National Key R&D Program of China ; National Key R&D Program of China ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; State Key Laboratory of Geo-Information Engineering ; State Key Laboratory of Geo-Information Engineering ; Fundamental Research Funds for the Central Universities, CHD ; Fundamental Research Funds for the Central Universities, CHD ; National Key R&D Program of China ; National Key R&D Program of China ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory ; Chinese Academy of Sciences college students innovation practice training program in Shanghai Astronomical Observatory
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000614805500001
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/79670
Collection中国科学院国家天文台
Corresponding AuthorXi, Jiangbo; Shao, Zhengyi
Affiliation1.Nanjing Normal Univ, Sch Geog, Nanjing 210023, Peoples R China
2.Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Peoples R China
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
4.Changan Univ, Sch Geol Engn & Geomat, Xian 710054, Shanxi, Peoples R China
5.Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China
6.Key Lab Astrophys, 100 Guilin Rd, Shanghai 200234, Peoples R China
Recommended Citation
GB/T 7714
Xiang, Yaobing,Xi, Jiangbo,Shao, Zhengyi,et al. Open clusters identifying by multi-scale density feature learning[J]. ASTROPHYSICS AND SPACE SCIENCE,2021,366(2):11.
APA Xiang, Yaobing,Xi, Jiangbo,Shao, Zhengyi,Wang, Min,&Yang, Yun.(2021).Open clusters identifying by multi-scale density feature learning.ASTROPHYSICS AND SPACE SCIENCE,366(2),11.
MLA Xiang, Yaobing,et al."Open clusters identifying by multi-scale density feature learning".ASTROPHYSICS AND SPACE SCIENCE 366.2(2021):11.
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