Applying Hybrid Clustering in Pulsar Candidate Sifting with Multi-modality for FAST Survey

作     者：Zi-Yi You Yun-Rong Pan Zhi Ma Li Zhang Shuo Xiao Dan-Dan Zhang Shi-Jun Dang Ru-Shuang Zhao Pei Wang Ai-Jun Dong Jia-Tao Jiang Ji-Bing Leng Wei-An Li Si-Yao Li 

作者机构：School of Physics and Electronic ScienceGuizhou Normal UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Radio Astronomy and Data ProcessingGuizhou Normal UniversityGuiyang 550025China College of Big Data and Information EngineeringGuizhou UniversityGuiyang 550025China CAS Key Laboratory of FASTNational Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China Key Laboratory of Information and Computing Guizhou ProvinceGuizhou Normal UniversityGuiyang 550001China School of Big data and Computer ScienceGuizhou Normal UniversityGuiyang 550025China Guizhou Software Engineering Research CenterGuiyang 550000China 

出 版 物：《Research in Astronomy and Astrophysics》 (天文和天体物理学研究（英文版）)

年 卷 期：2024年第24卷第3期

页      面：283-296页

核心收录：

学科分类：07[理学] 070401[理学-天体物理] 0704[理学-天文学] 

基　　金：supported by the National Key R&D Program of China(No.2022YFE0133700) the National Natural Science Foundation of China(NSFC,grant Nos.12273008,11963003,12273007 and 62062025) the National SKA Program of China(No.2020SKA0110300) the Guizhou Province Science and Technology Support Program(General Project) No.Qianhe SupportGeneral 333,Science and Technology Foundation of Guizhou Province(Key Program,No.1432) the Guizhou Provincial Science and Technology Projects(Nos.ZK143 and ZK304) the Cultivation project of Guizhou University(No.76) 

主　　题：methods data analysis-surveys-methods numerical 

摘      要：Pulsar search is always the basis of pulsar navigation,gravitational wave detection and other research ***,the volume of pulsar candidates collected by the Five-hundred-meter Aperture Spherical radio Telescope(FAST)shows an explosive growth rate that has brought challenges for its pulsar candidate filtering ***,the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification *** this study,a multi-modal and semi-supervised learning based on a pulsar candidate sifting algorithm is presented,which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for *** on both High Time Resolution Universe SurveyⅡ(HTRU2)and actual FAST observation data demonstrate that the proposed algorithm could excellently identify pulsars:On HTRU2,the precision and recall rates of its parallel mode reach0.981 and 0.988 *** FAST data,those of its parallel mode reach 0.891 and 0.961,meanwhile,the running time also significantly decreases with the increment of parallel nodes within ***,we can conclude that our algorithm could be a feasible idea for large scale pulsar candidate sifting for FAST drift scan observation.