Generating a radioheliograph image from SDO/AIA data with the machine learning method

作者：張沛錦,王傳兵,蒲冠杉

摘要：Radioheliograph images are essential for the study of solar short term activities and long term variations, while the continuity and granularity of radioheliograph data are not so ideal, due to the short visible time of the Sun and the complex electron-magnetic environment near the ground-based radio telescope. In this work, we develop a multi-channel input single-channel output neural network, which can generate radioheliograph image in microwave band from the Extreme Ultra-violet(EUV) observation of the Atmospheric Imaging Assembly(AIA) on board the Solar Dynamic Observatory(SDO). The neural network is trained with nearly 8 years of data of Nobeyama Radioheliograph(No RH) at 17 GHz and SDO/AIA from January 2011 to September 2018. The generated radioheliograph image is in good consistency with the well-calibrated No RH observation. SDO/AIA provides solar atmosphere images in multiple EUV wavelengths every 12 seconds from space, so the present model can fill the vacancy of limited observation time of microwave radioheliograph, and support further study of the relationship between the microwave and EUV emission.

發文機構：CAS Key Laboratory of Geospace Environment CAS Center for the Excellence in Comparative Planetology Anhui Mengcheng Geophysics National Observation and Research Station

關鍵詞：Sun:radioradiationmethods:observationalmethods:dataanalysis

分類號： TP3[自動化與計算機技術—計算機科學與技術]