论文标题
通过机器学习方法从SDO/AIA数据中生成放射力图像
Generate Radioheliograph Image from SDO/AIA Data with Machine Learning Method
论文作者
论文摘要
放射射击图像对于研究太阳短期活动和长期变化至关重要，而放射光仪数据的连续性和粒度并不是那么理想，因为太阳的短暂可见时间和地面射电望远镜附近的复杂电子磁环境。在这项工作中，我们开发了一个多通道输入单通道输出神经网络，该网络可以从极端超紫罗兰（EUV）观察大气成像组件（AIA）在板载板上的太阳能动态天线（SDO）中生成微波频带的放射射击图像。从2011年1月至2018年9月，对神经网络进行了将近8年的Nobeyama RoadyHeliograph（NORH）和SDO/AIA的数据。产生的Radioheliograph图像与良好的Norh观察结果一致。 SDO/AIA从空间中每12秒钟提供一次多个EUV波长的太阳大气图像，因此当前模型可以填补微波无线电射线仪的有限观察时间的空缺，并支持对微波和EUV发射之间的关系的进一步研究。
The radioheliograph image is essential for the study of solar short term activities and long term variations, while the continuity and granularity of radioheliograph data is 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 (NoRH) at 17 GHz and SDO/AIA from January 2011 to September 2018. The generated radioheliograph image is in good consistency with the well-calibrated NoRH 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.
