PMO OpenIR
Pulsar candidate recognition with deep learning
Zhang, Haoyuan1,2; Zhao, Zhen1; An, Tao1,3; Lao, Baoqiang1; Chen, Xiao1
2019
Source PublicationCOMPUTERS & ELECTRICAL ENGINEERING
ISSN0045-7906
Volume73Pages:1-8
Corresponding AuthorZhang, Haoyuan(zhy@shao.ac.cn)
AbstractIn this paper, we present a deep learning-based recognition algorithm to identify pulsars by observing data containing millions of candidates including radio frequency interference and noise sources. The dataset is obtained from the High Time Resolution Universe survey created and updated by the Parkes telescope. We investigate several effective single and combined features via simple logistic regression. To deal with the imbalanced dataset, we oversimplify the original dataset at different sampling rates, which is also one of the learning parameters. After training the pre-processed dataset via a convolutional neural network, we provide a cross-validated evaluation of all candidates. Results show that the deep-learning based recognition algorithm can identify the pulsar and radio frequency interference signals with high accuracy. The precision and recall of radio frequency interference are both 100%, and those of pulsars are 91% and 94%, respectively. (C) 2018 Elsevier Ltd. All rights reserved.
KeywordPulsar candidate classification Radio astronomy Machine learning Methods and techniques Convolutional neural network Square kilometer array
DOI10.1016/j.compeleceng.2018.10.016
WOS KeywordSELECTION
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Interdisciplinary Applications ; Engineering, Electrical & Electronic
WOS IDWOS:000458593900001
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://libir.pmo.ac.cn/handle/332002/18635
Collection中国科学院紫金山天文台
Corresponding AuthorZhang, Haoyuan
Affiliation1.Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Key Lab Radio Astron, Nanjing 210008, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Haoyuan,Zhao, Zhen,An, Tao,et al. Pulsar candidate recognition with deep learning[J]. COMPUTERS & ELECTRICAL ENGINEERING,2019,73:1-8.
APA Zhang, Haoyuan,Zhao, Zhen,An, Tao,Lao, Baoqiang,&Chen, Xiao.(2019).Pulsar candidate recognition with deep learning.COMPUTERS & ELECTRICAL ENGINEERING,73,1-8.
MLA Zhang, Haoyuan,et al."Pulsar candidate recognition with deep learning".COMPUTERS & ELECTRICAL ENGINEERING 73(2019):1-8.
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