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Improving Pulsar Candidate Identification with Grid Group Uniform Sampling

文献类型：期刊论文

作者Song, Yi-Ning1,2; Chen, Mao-Zheng1,3; Liu, Zhi-Yong1,3
刊名RESEARCH IN ASTRONOMY AND ASTROPHYSICS
出版日期2025-05-01
卷号25期号:5页码:055007
关键词(stars:) pulsars: general methods: data analysis methods: statistical
ISSN号1674-4527
DOI10.1088/1674-4527/adc85b
产权排序1
英文摘要Pulsar candidate identification is an indispensable task in pulsar science. Based on the characteristics of imbalanced and diverse pulsar data sets, and the lack of a unified processing framework, we first used dimensionality reduction and visualization to analyze potential deficiencies caused by the incompleteness of current data set extraction methods. We found that the limited use of non-pulsar data may lead to bias in the result, which may limit the generalization ability. Based on the dimensionality reduction results, we propose a Grid Group Uniform Sampling (GGUS) method. This data preprocessing method improves the performance of Random Forest, Support Vector Machine, Convolutional Neural Network, and ResNet50 models on Lyon's features, diagnostic plots, and period-dispersion measure (period-DM) plots in the HTRU1 data set. The average recall increased by approximately 0.5%, precision by nearly 2%, and F-1 score by around 1.2% for all models and in all data sets. In the period-DM plots testing, the high-performance ResNet50 algorithm achieved over 98% F-1 using random sampling. GGUS demonstrated further improvements in this test, enhancing the average F-1 score, precision, and recall by approximately 0.07%, 0.1%, and 0.03%, respectively.
WOS关键词CLASSIFICATION ; SELECTION
资助项目National Key Research and Development Program of China[2018YFA0404603] ; Operation, Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments ; Ministry of Finance of China (MOF)
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001485160500001
出版者IOP Publishing Ltd
资助机构National Key Research and Development Program of China ; Operation, Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments ; Ministry of Finance of China (MOF)
源URL[http://ir.xao.ac.cn/handle/45760611-7/7739]  
专题微波接收机技术实验室
通讯作者Chen, Mao-Zheng; Liu, Zhi-Yong
作者单位1.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Key Lab Radio Astron & Technol, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Song, Yi-Ning,Chen, Mao-Zheng,Liu, Zhi-Yong. Improving Pulsar Candidate Identification with Grid Group Uniform Sampling[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2025,25(5):055007.
APA Song, Yi-Ning,Chen, Mao-Zheng,&Liu, Zhi-Yong.(2025).Improving Pulsar Candidate Identification with Grid Group Uniform Sampling.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,25(5),055007.
MLA Song, Yi-Ning,et al."Improving Pulsar Candidate Identification with Grid Group Uniform Sampling".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 25.5(2025):055007.

入库方式： OAI收割

来源：新疆天文台

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