Pulsar candidate recognition with deep learning
文献类型：期刊论文
| 作者 | Zhang, Haoyuan1,2; Zhao, Zhen1; An, Tao1,3; Lao, Baoqiang1; Chen, Xiao1 |
| 刊名 | COMPUTERS & ELECTRICAL ENGINEERING
 |
| 出版日期 | 2019 |
| 卷号 | 73页码:1-8 |
| 关键词 | Pulsar candidate classification Radio astronomy Machine learning Methods and techniques Convolutional neural network Square kilometer array |
| ISSN号 | 0045-7906 |
| DOI | 10.1016/j.compeleceng.2018.10.016 |
| 英文摘要 | In 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. |
| 资助项目 | Ministry of Science and Technology of China[2016YFE0100300] ; Ministry of Science and Technology of China[SQ2018YFA040022] ; Chinese Academy of Sciences (CAS)[114231KYSB20170003] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000458593900001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.226.72/handle/331011/31921]  |
| 专题 | 中国科学院上海天文台 |
| 通讯作者 | Zhang, Haoyuan |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：上海天文台
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