Machine learning in solar physics

IF 20.9 1区 物理与天体物理 Living Reviews in Solar Physics Pub Date : 2023-07-13 DOI:10.1007/s41116-023-00038-x
Andrés Asensio Ramos, Mark C. M. Cheung, Iulia Chifu, Ricardo Gafeira
{"title":"Machine learning in solar physics","authors":"Andrés Asensio Ramos,&nbsp;Mark C. M. Cheung,&nbsp;Iulia Chifu,&nbsp;Ricardo Gafeira","doi":"10.1007/s41116-023-00038-x","DOIUrl":null,"url":null,"abstract":"<div><p>The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.</p></div>","PeriodicalId":49147,"journal":{"name":"Living Reviews in Solar Physics","volume":null,"pages":null},"PeriodicalIF":20.9000,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s41116-023-00038-x.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Living Reviews in Solar Physics","FirstCategoryId":"101","ListUrlMain":"https://link.springer.com/article/10.1007/s41116-023-00038-x","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
太阳物理学中的机器学习
机器学习在太阳物理学中的应用有可能极大地增强我们对太阳大气中发生的复杂过程的理解。通过使用深度学习等技术,我们现在能够分析来自太阳观测的大量数据,并识别出使用传统方法可能无法显现的模式和趋势。这可以帮助我们提高对太阳耀斑等爆炸性事件的理解,太阳耀斑会对地球环境产生强烈影响。预测地球上的危险事件对我们这个技术社会至关重要。机器学习还可以让我们更深入地研究数据,并提出更复杂的模型来解释它们,从而提高我们对太阳内部运作的理解。此外,机器学习的使用可以帮助自动分析太阳能数据,减少对人工劳动的需求,提高该领域的研究效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Living Reviews in Solar Physics
Living Reviews in Solar Physics ASTRONOMY & ASTROPHYSICS-
自引率
1.40%
发文量
3
期刊介绍: Living Reviews in Solar Physics, a platinum open-access journal, publishes invited reviews covering research across all areas of solar and heliospheric physics. It distinguishes itself by maintaining a collection of high-quality reviews regularly updated by the authors. Established in 2004, it was founded by the Max Planck Institute for Solar System Research (MPS). "Living Reviews®" is a registered trademark of Springer International Publishing AG.
期刊最新文献
Machine learning in solar physics Models for the long-term variations of solar activity A history of solar activity over millennia Waves in the lower solar atmosphere: the dawn of next-generation solar telescopes Extreme solar events
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1