Deep learning in solar astronomy / Long Xu, Yihua Yan, Xin Huang.
Xu, Long, author.; Yan, Yihua, author.; Huang, Xin, author.
2022
QB501
Available Online

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Title Deep learning in solar astronomy / Long Xu, Yihua Yan, Xin Huang.
Author Xu, Long, author.
ISBN 9789811927461 (electronic bk.)
9811927464 (electronic bk.)
9789811927454 (print)
DOI https://doi.org/10.1007/978-981-19-2746-1
Published Singapore : Springer, 2022.
Language English
Description 1 online resource (xiv, 92 pages) : illustrations.
Item Number 10.1007/978-981-19-2746-1 doi
Call Number QB501
Dewey Decimal Classification 523.70285631
Summary The volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition. Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices. This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed June 3, 2022).
Added Author Yan, Yihua, author.
Huang, Xin, author.
Series SpringerBriefs in computer science, 2191-5776
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Record Appears in Online Resources > Ebooks
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Chapter 1: Introduction
Chapter 2: Classical deep learning models
Chapter 3: Deep learning in solar image classification tasks
Chapter 4: Deep learning in solar object detection tasks
Chapter 5: Deep learning in solar image generation tasks
Chapter 6: Deep learning in solar forecasting tasks.

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