[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116875-en":3,"doc-seo-116875-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116875,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine learning in solar physics","Machine learning in solar physics enhances understanding of complex solar-atmosphere processes by enabling analysis of large observational datasets and extraction of hidden patterns through techniques such as deep learning. It supports improved insight into explosive phenomena like solar flares and their potential Earth impacts, strengthening hazard prediction for a technology-dependent society. The approach also enables more detailed physical modeling, increases research efficiency through automation of solar-data analysis, and reduces reliance on manual labor.","arXiv :2306 . 15308v1 [ astro-ph . SR] 27 Jun 2023  \nLiving Reviews in Solar Physics manuscript No.  \n(will be inserted by the editor)  \nMachine learning in solar physics  \nAndr􀀓es Asensio Ramos 􀀁 Mark  \nC. M. Cheung 􀀁 Iulia Chifu 􀀁 Ricardo  \nGafeira  \nReceived: date / Accepted: date  \nAbstract 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 􀀍ares, which can have a strong e􀀋ect 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 e􀀎ciency of research in this 􀀌eld.  \nKeywords Sun: general, photosphere, chromosphere, corona, activity 􀀁  \nMethods: data analysis, statistical 􀀁 Techniques: image processing  \nA. Asensio Ramos  \nInstituto de Astrof􀀓􀀐sica de Canarias, 38205, La Laguna, Tenerife, Spain  \nDepartamento de Astrof􀀓􀀐sica, Universidad de La Laguna, 38205 La Laguna, Tenerife, Spain E-mail: [aasensio@iac.es](aasensio@iac.es)  \n[M. C. M. Cheung](M. C. M. Cheung)  \nCSIRO, Space & Astronomy, PO Box 76, Epping, NSW 1710, Australia [E-mail: mark.cheung@csiro.au](E-mail: mark.cheung@csiro.au)  \n[I. Chifu](I. Chifu)  \nInstitute for Astrophysics and Geophysics, University of G􀁿ottingen, Friedrich-Hund-Platz 1, 37077 G􀁿ottingen, Germany  \nE-mail: [iulia.chifu@uni-goettingen.de](iulia.chifu@uni-goettingen.de)  \nR. Gafeira  \nInstituto de Astrof􀀓􀀐sica e Ci^encias do Espa􀀘co, Departamento de F􀀓􀀐sica, Universidade de Coimbra, OGAUC, Rua do Observat􀀓orio s/n, 3040-004 Coimbra, Portugal  \nE-mail: [gafeira@uc.pt](gafeira@uc.pt)  \nContents  \n1 Introduction ........................................ 3  \n1.1 Supervised learning ................................. 5  \n1.1.1 Classi􀀌cation vs. regression ........................ 7  \n1.1.2 Data partitioning .............................. 7  \n1.1.3 Encoders and decoders ........................... 8  \n1.2 Unsupervised learning ............................... 8  \n1.3 Reinforcement learning ............................... 9  \n2 Some ideas about dimensionality ............................ 9  \n3 Linear models: unsupervised ............................... 12  \n3.1 Principal component analysis ........................... 12  \n3.1.1 Denoising .................................. 13  \n3.1.2 Interpretability ............................... 14  \n3.1.3 Inversion with lookup tables ........................ 15  \n3.2 Fuzzy clustering .................................. 17  \n3.3 k-means ....................................... 20  \n3.3.1 Spectral clustering ............................. 21  \n3.3.2 Segmentation of coronal holes ....................... 22  \n4 Linear models: supervised ................................ 22  \n4.1 Hermite functions .................................. 23  \n4.2 Relevance vector machines ............................. 23  \n4.3 Compressed sensing and sparsity regularization ................. 24  \n5 Deep neural networks .................................. 29  \n5.1 Architectures .................................... 31  \n5.1.1 Multi-layer fully connected neural networks ............... 31  \n5.1.2 Convolutional neural networks ...................... 32  \n5.1.3 Recurrent neural networks ......................... 34  \n5.1.4 Attention and Transformers ........................ 34  \n5.1.5 Graph neural networks ......","cbCail733RM2dXzg","https://ap.wps.com/l/cbCail733RM2dXzg","pdf",5253143,1,100,"English","en",105,"# Introduction\n## Supervised learning\n## Unsupervised learning\n## Reinforcement learning\n# Some ideas about dimensionality\n# Linear models: unsupervised\n## Principal component analysis\n## Fuzzy clustering\n## k-means\n# Linear models: supervised\n# Deep neural networks\n## Architectures\n## Activation layers\n## Training\n# Unsupervised deep learning\n# Applications of supervised deep learning","[{\"question\":\"How does machine learning improve analysis of solar observations?\",\"answer\":\"Machine learning methods, especially deep learning, analyze large volumes of solar observational data to detect patterns and trends that traditional approaches may miss.\"},{\"question\":\"Why is predicting solar flares and hazardous events on Earth important?\",\"answer\":\"Solar flares can strongly affect the Earth environment, so improving predictive capabilities supports safeguarding technological society.\"},{\"question\":\"In what ways can machine learning increase research efficiency in solar physics?\",\"answer\":\"It can automate parts of solar-data analysis, reducing the need for manual labor and improving the efficiency of research workflows.\"}]","Machine learning in solar physics | 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does machine learning improve analysis of solar observations?","Question",{"text":76,"@type":77},"Machine learning methods, especially deep learning, analyze large volumes of solar observational data to detect patterns and trends that traditional approaches may miss.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why is predicting solar flares and hazardous events on Earth important?",{"text":81,"@type":77},"Solar flares can strongly affect the Earth environment, so improving predictive capabilities supports safeguarding technological society.",{"name":83,"@type":74,"acceptedAnswer":84},"In what ways can machine learning increase research efficiency in solar physics?",{"text":85,"@type":77},"It can automate parts of solar-data analysis, reducing the need for manual labor and improving the efficiency of research 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