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Imbalanced Learning for RR Lyrae Stars Based on SDSS and GALEX Databases

文献类型：期刊论文

作者Zhang, Jingyi1,2; Zhang, Yanxia1; Zhao, Yongheng1
刊名ASTRONOMICAL JOURNAL
出版日期2018-03-01
卷号155期号:3页码:8
关键词astronomical databases: miscellaneous methods: data analysis methods: statistical stars: general stars: variables: RR Lyrae
ISSN号0004-6256
DOI10.3847/1538-3881/aaa5b1
英文摘要We apply machine learning and Convex-Hull algorithms to separate RR Lyrae stars from other stars like main-sequence stars, white dwarf stars, carbon stars, CVs, and carbon-lines stars, based on the Sloan Digital Sky Survey and Galaxy Evolution Explorer (GALEX). In low-dimensional spaces, the Convex-Hull algorithm is applied to select RR Lyrae stars. Given different input patterns of (u - g, g - r), (g - r, r - i), (r - i, i - z), (u - g, g - r, r - i), (g - r, r - i, i - z), (u - g, g - r, i - z), and (u - g, r - i, i - z), different convex hulls can be built for RR Lyrae stars. Comparing the performance of different input patterns, u - g, g - r, i - z is the best input pattern. For this input pattern, the efficiency (the fraction of true RR Lyrae stars in the predicted RR Lyrae sample) is 4.2% with a completeness (the fraction of recovered RR Lyrae stars in the whole RR Lyrae sample) of 100%, increases to 9.9% with 97% completeness and to 16.1% with 53% completeness by removing some outliers. In high-dimensional spaces, machine learning algorithms are used with input patterns (u - g, g - r, r - i, i - z), (u - g, g - r, r - i, i - z, r), (NUV - u, u - g, g - r, r - i, i - z), and (NUV - u, u - g, g - r, r - i, i - z, r). RR Lyrae stars, which belong to the class of interest in our paper, are rare compared to other stars. For the highly imbalanced data, cost-sensitive Support Vector Machine, cost-sensitive Random Forest, and Fast Boxes is used. The results show that information from GALEX is helpful for identifying RR Lyrae stars, and Fast Boxes is the best performer on the skewed data in our case.
WOS关键词DIGITAL SKY SURVEY ; CLASSIFICATION ; OBJECTS
资助项目973 Program[2014CB845700] ; National Natural Science Foundation of China[U1731109] ; Alfred P. Sloan Foundation ; U.S. Department of Energy Office of Science ; Center for High-Performance Computing at the University of Utah ; Brazilian Participation Group ; Carnegie Institution for Science, Carnegie Mellon University ; Chilean Participation Group ; French Participation Group ; Harvard-Smithsonian Center for Astrophysics ; Instituto de Astrofisica de Canarias ; Johns Hopkins University ; Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo ; Lawrence Berkeley National Laboratory ; Leibniz Institut fur Astrophysik Potsdam (AIP) ; Max-Planck-Institut fur Astronomie (MPIA Heidelberg) ; Max-Planck-Institut fur Astrophysik (MPA Garching) ; Max-Planck-Institut fur Extraterrestrische Physik (MPE) ; National Astronomical Observatories of China ; New Mexico State University ; New York University ; University of Notre Dame ; Observatario Nacional/MCTI ; Ohio State University ; Pennsylvania State University ; Shanghai Astronomical Observatory ; United Kingdom Participation Group ; Universidad Nacional Autonoma de Mexico ; University of Arizona ; University of Colorado Boulder ; University of Oxford ; University of Portsmouth ; University of Utah ; University of Virginia ; University of Washington ; University of Wisconsin ; Vanderbilt University ; Yale University
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:000424694800003
出版者IOP PUBLISHING LTD
资助机构973 Program ; National Natural Science Foundation of China ; Alfred P. Sloan Foundation ; U.S. Department of Energy Office of Science ; Center for High-Performance Computing at the University of Utah ; Brazilian Participation Group ; Carnegie Institution for Science, Carnegie Mellon University ; Chilean Participation Group ; French Participation Group ; Harvard-Smithsonian Center for Astrophysics ; Instituto de Astrofisica de Canarias ; Johns Hopkins University ; Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo ; Lawrence Berkeley National Laboratory ; Leibniz Institut fur Astrophysik Potsdam (AIP) ; Max-Planck-Institut fur Astronomie (MPIA Heidelberg) ; Max-Planck-Institut fur Astrophysik (MPA Garching) ; Max-Planck-Institut fur Extraterrestrische Physik (MPE) ; National Astronomical Observatories of China ; New Mexico State University ; New York University ; University of Notre Dame ; Observatario Nacional/MCTI ; Ohio State University ; Pennsylvania State University ; Shanghai Astronomical Observatory ; United Kingdom Participation Group ; Universidad Nacional Autonoma de Mexico ; University of Arizona ; University of Colorado Boulder ; University of Oxford ; University of Portsmouth ; University of Utah ; University of Virginia ; University of Washington ; University of Wisconsin ; Vanderbilt University ; Yale University ; 973 Program ; National Natural Science Foundation of China ; Alfred P. Sloan Foundation ; U.S. Department of Energy Office of Science ; Center for High-Performance Computing at the University of Utah ; Brazilian Participation Group ; Carnegie Institution for Science, Carnegie Mellon University ; Chilean Participation Group ; French Participation Group ; Harvard-Smithsonian Center for Astrophysics ; Instituto de Astrofisica de Canarias ; Johns Hopkins University ; Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo ; Lawrence Berkeley National Laboratory ; Leibniz Institut fur Astrophysik Potsdam (AIP) ; Max-Planck-Institut fur Astronomie (MPIA Heidelberg) ; Max-Planck-Institut fur Astrophysik (MPA Garching) ; Max-Planck-Institut fur Extraterrestrische Physik (MPE) ; National Astronomical Observatories of China ; New Mexico State University ; New York University ; University of Notre Dame ; Observatario Nacional/MCTI ; Ohio State University ; Pennsylvania State University ; Shanghai Astronomical Observatory ; United Kingdom Participation Group ; Universidad Nacional Autonoma de Mexico ; University of Arizona ; University of Colorado Boulder ; University of Oxford ; University of Portsmouth ; University of Utah ; University of Virginia ; University of Washington ; University of Wisconsin ; Vanderbilt University ; Yale University ; 973 Program ; National Natural Science Foundation of China ; Alfred P. Sloan Foundation ; U.S. Department of Energy Office of Science ; Center for High-Performance Computing at the University of Utah ; Brazilian Participation Group ; Carnegie Institution for Science, Carnegie Mellon University ; Chilean Participation Group ; French Participation Group ; Harvard-Smithsonian Center for Astrophysics ; Instituto de Astrofisica de Canarias ; Johns Hopkins University ; Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo ; Lawrence Berkeley National Laboratory ; Leibniz Institut fur Astrophysik Potsdam (AIP) ; Max-Planck-Institut fur Astronomie (MPIA Heidelberg) ; Max-Planck-Institut fur Astrophysik (MPA Garching) ; Max-Planck-Institut fur Extraterrestrische Physik (MPE) ; National Astronomical Observatories of China ; New Mexico State University ; New York University ; University of Notre Dame ; Observatario Nacional/MCTI ; Ohio State University ; Pennsylvania State University ; Shanghai Astronomical Observatory ; United Kingdom Participation Group ; Universidad Nacional Autonoma de Mexico ; University of Arizona ; University of Colorado Boulder ; University of Oxford ; University of Portsmouth ; University of Utah ; University of Virginia ; University of Washington ; University of Wisconsin ; Vanderbilt University ; Yale University ; 973 Program ; National Natural Science Foundation of China ; Alfred P. Sloan Foundation ; U.S. Department of Energy Office of Science ; Center for High-Performance Computing at the University of Utah ; Brazilian Participation Group ; Carnegie Institution for Science, Carnegie Mellon University ; Chilean Participation Group ; French Participation Group ; Harvard-Smithsonian Center for Astrophysics ; Instituto de Astrofisica de Canarias ; Johns Hopkins University ; Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo ; Lawrence Berkeley National Laboratory ; Leibniz Institut fur Astrophysik Potsdam (AIP) ; Max-Planck-Institut fur Astronomie (MPIA Heidelberg) ; Max-Planck-Institut fur Astrophysik (MPA Garching) ; Max-Planck-Institut fur Extraterrestrische Physik (MPE) ; National Astronomical Observatories of China ; New Mexico State University ; New York University ; University of Notre Dame ; Observatario Nacional/MCTI ; Ohio State University ; Pennsylvania State University ; Shanghai Astronomical Observatory ; United Kingdom Participation Group ; Universidad Nacional Autonoma de Mexico ; University of Arizona ; University of Colorado Boulder ; University of Oxford ; University of Portsmouth ; University of Utah ; University of Virginia ; University of Washington ; University of Wisconsin ; Vanderbilt University ; Yale University
源URL[http://ir.bao.ac.cn/handle/114a11/35694]  
专题中国科学院国家天文台
通讯作者Zhang, Yanxia
作者单位1.Chinese Acad Sci, Natl Astron Observ, Key Lab Opt Astron, Beijing 100012, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Jingyi,Zhang, Yanxia,Zhao, Yongheng. Imbalanced Learning for RR Lyrae Stars Based on SDSS and GALEX Databases[J]. ASTRONOMICAL JOURNAL,2018,155(3):8.
APA Zhang, Jingyi,Zhang, Yanxia,&Zhao, Yongheng.(2018).Imbalanced Learning for RR Lyrae Stars Based on SDSS and GALEX Databases.ASTRONOMICAL JOURNAL,155(3),8.
MLA Zhang, Jingyi,et al."Imbalanced Learning for RR Lyrae Stars Based on SDSS and GALEX Databases".ASTRONOMICAL JOURNAL 155.3(2018):8.

入库方式： OAI收割

来源：国家天文台

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