Deep Learning in Solar Astronomy - SpringerBriefs in Computer Science - Long Xu - Libros - Springer Verlag, Singapore - 9789811927454 - 28 de mayo de 2022
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Long Xu
Deep Learning in Solar Astronomy - SpringerBriefs in Computer Science 1st ed. 2022 edition

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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.
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92 pages, 37 Tables, color; 1 Illustrations, black and white; XIV, 92 p. 1 illus.

Medios de comunicación Libros     Paperback Book   (Libro con tapa blanda y lomo encolado)
Publicado 28 de mayo de 2022
ISBN13 9789811927454
Editores Springer Verlag, Singapore
Páginas 92
Dimensiones 150 × 220 × 10 mm   ·   180 g
Lengua Inglés  

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