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作 者:Huanbin Chi Zebang Lai Feng Wang Zhongmu Li Ying Mei
机构地区:[1]Center for Astrophysics,Guangzhou University,Guangzhou 510006,China [2]Peng Cheng Laboratory,Shenzhen 518000,China [3]Institute of Astronomy and Information,Dali University,Dali 671000,China
出 处:《Research in Astronomy and Astrophysics》2024年第11期243-258,共16页天文和天体物理学研究(英文版)
基 金:supported by the National Key Research And Development Program of China(No.2022YFF0711500);the National Natural Science Foundation of China(NSFC,Grant No.12373097);the Basic and Applied Basic Research Foundation Project of Guangdong Province(No.2024A1515011503);the Guangzhou Science and Technology Funds(2023A03J0016)。
摘 要:Open clusters(OCs)serve as invaluable tracers for investigating the properties and evolution of stars and galaxies.Despite recent advancements in machine learning clustering algorithms,accurately discerning such clusters remains challenging.We re-visited the 3013 samples generated with a hybrid clustering algorithm of FoF and pyUPMASK.A multi-view clustering(MvC)ensemble method was applied,which analyzes each member star of the OC from three perspectives—proper motion,spatial position,and composite views—before integrating the clustering outcomes to deduce more reliable cluster memberships.Based on the MvC results,we further excluded cluster candidates with fewer than ten member stars and obtained 1256 OC candidates.After isochrone fitting and visual inspection,we identified 506 candidate OCs in the Milky Way.In addition to the 493 previously reported candidates,we finally discovered 13 high-confidence new candidate clusters.
关 键 词:GALAXIES star clusters GENERAL (Galaxy:)open clusters and associations GENERAL methods data analysis
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