Overview
- Explore techniques of deep learning to scientific research of solar astronomy, including applications of deep learning.
- Present datasets of solar activity events and training samples for training deep learning models.
- Provide resources, including algorithm design, network design, competition with methods, databases and source codecs.
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (6 chapters)
Keywords
About this book
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.
Reviews
Authors and Affiliations
About the authors
Prof. Yihua Yan received his Ph.D. degree from the Dalian University of Technology in 1990. He was a Foreign Research Fellow with the NAOJ (Japan) from 1995 to 1996, and an Alexander von Humboldt Fellow with the Astronomical Institute, Wurzburg University, Germany, from 1996 to 1997. He was the President of IAU Division E: Sun and Heliosphere from 2015 to 2018. He was the Director of the CAS Key Laboratory of Solar Activity (2008-2019), and the Director of Solar Physics Division (2013-2021), at NAOC. He is currently a Professor and a Chief Scientist, National Space Science Center, Chinese Academy of Sciences.
Dr. Xin Huang received the Ph.D. degree from Harbin Institute of Technology in 2010. He was an associate professor at Solar Activity Prediction Center, NAOC from 2013. Now, he is with the Space Environment Prediction Center, National Space Science Center, Chinese Academy of Sciences. His research interests include data mining, image processing and short-term solar activity forecasting. He has published more than 20 academic papers, including one of the top 1% most cited papers in IOP Publishing’s astrophysics journals, published over the period of 2018-2020.
Bibliographic Information
Book Title: Deep Learning in Solar Astronomy
Authors: Long Xu, Yihua Yan, Xin Huang
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-981-19-2746-1
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
Softcover ISBN: 978-981-19-2745-4Published: 28 May 2022
eBook ISBN: 978-981-19-2746-1Published: 27 May 2022
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XIV, 92
Number of Illustrations: 1 b/w illustrations
Topics: Astronomy, Astrophysics and Cosmology, Astronomy, Observations and Techniques, Machine Learning, Computer Imaging, Vision, Pattern Recognition and Graphics, Image Processing and Computer Vision