基于深度学习的公制 II 型射电发射中受 CME 驱动的冲击距离预测

IF 1.8 4区 物理与天体物理 Q3 ASTRONOMY & ASTROPHYSICS Astrophysics and Space Science Pub Date : 2024-06-01 DOI:10.1007/s10509-024-04319-1
Kwabena Kyeremateng, Amr Hamada, Ahmed Elsaid, Ayman Mahrous
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Deep learning-based prediction of CME-driven shock standoff distances in metric type II radio emissions
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来源期刊
Astrophysics and Space Science
Astrophysics and Space Science 地学天文-天文与天体物理
CiteScore
3.40
自引率
5.30%
发文量
106
审稿时长
2-4 weeks
期刊介绍: Astrophysics and Space Science publishes original contributions and invited reviews covering the entire range of astronomy, astrophysics, astrophysical cosmology, planetary and space science and the astrophysical aspects of astrobiology. This includes both observational and theoretical research, the techniques of astronomical instrumentation and data analysis and astronomical space instrumentation. We particularly welcome papers in the general fields of high-energy astrophysics, astrophysical and astrochemical studies of the interstellar medium including star formation, planetary astrophysics, the formation and evolution of galaxies and the evolution of large scale structure in the Universe. Papers in mathematical physics or in general relativity which do not establish clear astrophysical applications will no longer be considered. The journal also publishes topically selected special issues in research fields of particular scientific interest. These consist of both invited reviews and original research papers. Conference proceedings will not be considered. All papers published in the journal are subject to thorough and strict peer-reviewing. Astrophysics and Space Science features short publication times after acceptance and colour printing free of charge.
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