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| 基于卷积神经网络的快速射电暴候选体分类 | |
| Alternative Title | Fast Radio Burst Candidate Classification with Convolutional Neural Networks |
刘艳玲1,2,3,4 ; 陈卯蒸1,2,3,4; 李健1,3,4; 闫浩1,3,4; 袁建平1,2,3
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| 2022-07-01 | |
| Source Publication | 天文学报
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| ISSN | 0001-5245 |
| Volume | 63Issue:4Pages:107-116 |
| Contribution Rank | 1 |
| Abstract | 针对目前从海量的快速射电暴(Fast Radio Burst, FRB)候选体中人工筛选FRB事件难以为继的现状,提出了一种基于卷积神经网络(Convolutional Neural Networks, CNN)的FRB候选体分类方法.首先,通过真实的观测数据和仿真FRB组成训练和测试样本集.其次,建立了二输入的深度卷积神经网络模型,并对其进行训练、测试和优化,获取FRB候选体分类器.最后,利用来自脉冲星的单脉冲数据对该分类器的有效性和性能进行了验证.实验结果表明,该方法可以快速从大量候选体中准确识别出单脉冲事件,极大地提高了FRB候选体的处理速率和效率. |
| Other Abstract | Manually identifying fast radio burst (FRB) events from the massive candidates by a human is a laborious and time-consuming task.It's an unsustainable working mode for the constantly growing volume of observation data.In this paper,we present a method of FRB candidates classification based on convolutional neural networks (CNN).First,we build training and test sets with real observation data and simulated FRBs.Second,a two-input deep convolutional neural network model is constructed,trained and optimized,and the FRB candidate classifier is obtained.Then,the effectiveness and performance of the classifier are tested and verified by using single pulses from pulsar.Experiment results show that this method can quickly and accurately identify single pulse events from candidates,which greatly improves the processing speed and efficiency of FRB candidates. |
| Keyword | 射电连续谱:暂现源 方法:数据分析 方法:分类 |
| DOI | 10.15940/j.cnki.0001-5245.2022.04.011 |
| URL | 查看原文 |
| Indexed By | CSCD ; 中文核心期刊要目总览 |
| Language | 中文 |
| CSCD ID | CSCD:7271935 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.xao.ac.cn/handle/45760611-7/5096 |
| Collection | 射电天文研究室_数字技术实验室 |
| Corresponding Author | 陈卯蒸 |
| Affiliation | 1.中国科学院新疆天文台乌鲁木齐830011; 2.中国科学院大学北京100049; 3.中国科学院射电天文重点实验室南京210023; 4.新疆微波技术重点实验室乌鲁木齐830011 |
| First Author Affilication | Xinjiang Astronomical Observatory, Chinese Academy of Sciences |
| Corresponding Author Affilication | Xinjiang Astronomical Observatory, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | 刘艳玲,陈卯蒸,李健,等. 基于卷积神经网络的快速射电暴候选体分类[J]. 天文学报,2022,63(4):107-116. |
| APA | 刘艳玲,陈卯蒸,李健,闫浩,&袁建平.(2022).基于卷积神经网络的快速射电暴候选体分类.天文学报,63(4),107-116. |
| MLA | 刘艳玲,et al."基于卷积神经网络的快速射电暴候选体分类".天文学报 63.4(2022):107-116. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 刘艳玲-2022-基于卷积神经网络的快速（1217KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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