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| 基于机器学习的快速射电暴搜寻方法综述 | |
| Alternative Title | A Review of Fast Radio Burst Search Methods Based on Machine Learning |
刘艳玲1,2,3,4 ; 陈卯蒸1,2,3,4; 袁建平1,2,3
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| 2022-09-01 | |
| Source Publication | 天文研究与技术
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| ISSN | 1672-7673 |
| Volume | 19Issue:5Pages:509-517 |
| Contribution Rank | 1 |
| Abstract | 快速射电暴（Fast Radio Burst，FRB）是目前射电天文领域的主要热点前沿。其相关研究被《自然》（Nature）杂志评选为2020年十大科学发现之一。FRB爆发时间极短且鲜少重复的特点，使其观测捕捉到的概率极低。由人工从海量的天文观测数据中识别FRB事件是件耗时费力的工作。机器学习技术的蓬勃发展为实时搜寻与多频段联合跟踪观测FRB带来了可能。该文从传统机器学习方法和深度学习方法两个方面，对该研究已有的成果进行了分析与总结，并探讨了基于机器学习的FRB搜寻技术目前存在的问题和面临的挑战，分析了其未来发展趋势。 |
| Other Abstract | Fast Radio Bursts( FRBs) are a hot topic in the field of astronomy at present. Its related research was also selected by the journal Nature as one of the top 10 scientific discoveries of 2020. The characteristics that FRBs are millisecond-duration and rarely repeated make them hard to be captured. Identifying FRBs from massive astronomical observation data by human review is a time-consuming and laborious task. With the rapid development of machine learning technology,it is possible to carry out a realtime search and multi-frequency tracking for FRB events. This paper analyzes and summarizes the existing representative results from two aspects: traditional machine learning method and deep learning method. Finally,the existing problems and challenges of FRB search technology based on machine learning are discussed,and future development trend is also analyzed. In the near future,deep learning technology will be more widely used and become a powerful tool to search for FRBs efficiently. |
| Keyword | 快速射电暴 机器学习 搜寻方法 深度学习 射电天文 |
| DOI | 10.14005/j.cnki.issn1672-7673.20210916.001 |
| URL | 查看原文 |
| Indexed By | CSCD |
| Language | 中文 |
| CSCD ID | CSCD:7304909 |
| Citation statistics |
Cited Times:1[CSCD]
[CSCD Record]
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| Document Type | 期刊论文 |
| Identifier | http://ir.xao.ac.cn/handle/45760611-7/5097 |
| 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 新疆 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 江苏 南京 210033; 4.新疆微波技术重点实验室 新疆 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,袁建平. 基于机器学习的快速射电暴搜寻方法综述[J]. 天文研究与技术,2022,19(5):509-517. |
| APA | 刘艳玲,陈卯蒸,&袁建平.(2022).基于机器学习的快速射电暴搜寻方法综述.天文研究与技术,19(5),509-517. |
| MLA | 刘艳玲,et al."基于机器学习的快速射电暴搜寻方法综述".天文研究与技术 19.5(2022):509-517. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 刘艳玲-2022-基于机器学习的快速射电（5127KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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