A deep learning method for the recognition of solar radio burst spectrum.
A deep learning method for the recognition of solar radio burst spectrum.
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一种太阳射电爆发光谱识别的深度学习方法
DOI：
10.7717/peerj-cs.855
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Wang S
中科院分区：
文献类型：
--
作者：
Guo JC;Yan FB;Wan G;Hu XJ;Wang S
太阳辐射是地球大气层中影响天气的激发源，而耀斑、日冕物质抛射等一些太阳活动往往伴随着射电暴。太阳射电爆发的频谱有助于天文学家探索射电爆发的机制。随着太阳射电频谱观测方法的发展和进步，对太阳的观测几乎可以在一天中的任何时刻进行。如何从庞大的观测数据库中快速、自动地识别出小比例的突发数据成为一个重要的研究方向。本研究的创新点在于对样本分布不均衡的原始射电频谱数据集进行增强，并在此基础上提出了太阳射电频谱图像分类的神经网络模型。这种联合卷积和记忆单元的混合结构克服了传统卷积或记忆模型只能提取图像的片面特征的缺点。通过同时提取频谱结构特征和时间序列特征，增强了对频谱图像小特征的敏感性。基于中国太阳宽带射电光谱仪（SBRS）数据，该网络模型可将光谱图像的平均分类精度提高到98.73%，这将有助于相关天文研究。
Solar radiation is the excitation source that affects the weather in the atmosphere of the earth, and some solar activities such as flares and coronal mass ejections are often accompanied by radio bursts. The spectrum of solar radio bursts is helpful for astronomers to explore the mechanism of radio bursts. With the development and progress of solar radio spectrum observation methods, the observation of the Sun can be done at almost all times of day. How to quickly and automatically identify the small proportion of burst data from the huge corpus of observation data has become an important research direction. The innovation of this study is to enhance the original radio spectrum dataset with unbalanced sample distribution, and a neural network model for solar radio spectrum image classification is proposed on this basis. This hybrid structure of joint convolution and a memory unit overcomes the shortcoming of the traditional convolution or memory model, which can only extract one-sided features of an image. By extracting the frequency structure features and time-series features at the same time, the sensitivity to the small features of the spectrum image can be enhanced. Based on the data of the Solar Broadband Radio Spectrometer (SBRS) in China, the proposed network model can improve the average classification accuracy of the spectrum image to 98.73%, which will be helpful for related astronomical research.