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Pulsar candidate classification with deep convolutional neural networks
Wang, Yuan-Chao1,2; Li, Ming-Tao1,2; Pan, Zhi-Chen3,4,5; Zheng, Jian-Hua1,2
2019-09-01
Source PublicationRESEARCH IN ASTRONOMY AND ASTROPHYSICS
ISSN1674-4527
Volume19Issue:9Pages:10
AbstractAs the performance of dedicated facilities has continually improved, large numbers of pulsar candidates are being received, which makes selecting valuable pulsar signals from the candidates challenging. In this paper, we describe the design for a deep convolutional neural network (CNN) with 11 layers for classifying pulsar candidates. Compared to artificially designed features, the CNN chooses the sub-integrations plot and sub-bands plot for each candidate as inputs without carrying biases. To address the imbalance problem, a data augmentation method based on synthetic minority samples is proposed according to the characteristics of pulsars. The maximum pulses of pulsar candidates were first translated to the same position. and then new samples were generated by adding up multiple subplots of pulsars. The data augmentation method is simple and effective for obtaining varied and representative samples which keep pulsar characteristics. In experiments on the HTRU 1 dataset, it is shown that this model can achieve recall of 0.962 and precision of 0.963.
Keywordpulsars: general methods: statistical methods: data analysis
DOI10.1088/1674-4527/19/9/133
WOS KeywordDISCOVERY ; SELECTION ; SYSTEM
Language英语
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000485147000011
PublisherNATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/27729
Collection中国科学院国家天文台
Corresponding AuthorLi, Ming-Tao
Affiliation1.Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Natl Astron Observ, Beijing 100101, Peoples R China
4.Chinese Acad Sci, Ctr Astron Mega Sci, Beijing 100101, Peoples R China
5.Chinese Acad Sci, Natl Astron Observ, CAS Key Lab FAST, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Wang, Yuan-Chao,Li, Ming-Tao,Pan, Zhi-Chen,et al. Pulsar candidate classification with deep convolutional neural networks[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2019,19(9):10.
APA Wang, Yuan-Chao,Li, Ming-Tao,Pan, Zhi-Chen,&Zheng, Jian-Hua.(2019).Pulsar candidate classification with deep convolutional neural networks.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,19(9),10.
MLA Wang, Yuan-Chao,et al."Pulsar candidate classification with deep convolutional neural networks".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 19.9(2019):10.
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