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Deep Learning in Solar Astronomy

Af: Xin Huang, Long Xu, Yihua Yan Engelsk Paperback

Deep Learning in Solar Astronomy

Af: Xin Huang, Long Xu, Yihua Yan Engelsk Paperback
Tjek vores konkurrenters priser

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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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.

Produktdetaljer
Sprog: Engelsk
Sider: 92
ISBN-13: 9789811927454
Indbinding: Paperback
Udgave:
ISBN-10: 9811927456
Kategori: Machine learning
Udg. Dato: 28 maj 2022
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Verlag, Singapore
Oplagsdato: 28 maj 2022
Forfatter(e): Xin Huang, Long Xu, Yihua Yan
Forfatter(e) Xin Huang, Long Xu, Yihua Yan


Kategori Machine learning


ISBN-13 9789811927454


Sprog Engelsk


Indbinding Paperback


Sider 92


Udgave


Længde 0mm


Bredde 155mm


Højde 235mm


Udg. Dato 28 maj 2022


Oplagsdato 28 maj 2022


Forlag Springer Verlag, Singapore

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