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Proxy-based Prediction of Solar Extreme Ultraviolet Emission Using Deep Learning
The Astrophysical Journal Letters ( IF 7.9 ) Pub Date : 2021-04-06 , DOI: 10.3847/2041-8213/abee89
Anthony Pineci 1 , Peter Sadowski 2 , Eric Gaidos 3 , Xudong Sun 4
Affiliation  

High-energy radiation from the Sun governs the behavior of Earth’s upper atmosphere and such radiation from any planet-hosting star can drive the long-term evolution of a planetary atmosphere. However, much of this radiation is unobservable because of absorption by Earth’s atmosphere and the interstellar medium. This motivates the identification of a proxy that can be readily observed from the ground. Here, we evaluate absorption in the near-infrared 1083 nm triplet line of neutral orthohelium as a proxy for extreme ultraviolet (EUV) emission in the 30.4 nm line of He ii and 17.1 nm line of Fe ix from the Sun. We apply deep learning to model the nonlinear relationships, training and validating the model on historical, contemporaneous images of the solar disk acquired in the triplet He i line by the ground-based SOLIS observatory and in the EUV by the NASA Solar Dynamics Observatory. The model is a fully convolutional neural network that incorporates spatial information and accounts for the projection of the spherical Sun to 2d images. Using normalized target values, results indicate a median pixelwise relative error of 20% and a mean disk-integrated flux error of 7% on a held-out test set. Qualitatively, the model learns the complex spatial correlations between He i absorption and EUV emission has a predictive ability superior to that of a pixel-by-pixel model; it can also distinguish active regions from high-absorption filaments that do not result in EUV emission.



中文翻译:

使用深度学习基于代理的太阳极紫外辐射预测

来自太阳的高能辐射控制着地球高层大气的行为,来自任何行星宿主恒星的这种辐射可以推动行星大气的长期演化。然而,由于地球大气层和星际介质的吸收,大部分辐射是无法观察到的。这激发了对可以从地面容易观察到的代理的识别。在这里,我们评估了中性正氦的近红外 1083 nm 三重线中的吸收,作为 He ii 30.4 nm 线和 Fe ix 17.1 nm 线中极紫外 (EUV) 发射的代表从太阳。我们应用深度学习来模拟非线性关系,训练和验证模型的历史、同期太阳盘图像,这些图像是由地基 SOLIS 天文台在三重 He i线和美国宇航局太阳动力学天文台在 EUV 中获得的。该模型是一个完全卷积的神经网络,它结合了空间信息并考虑了球形太阳到二维图像的投影。使用归一化的目标值,结果表明在保留的测试集上,平均像素相对误差为 20%,平均磁盘积分通量误差为 7%。定性地,该模型学习了 He i之间的复杂空间相关性。吸收和 EUV 发射具有优于逐像素模型的预测能力;它还可以将活性区域与不会导致 EUV 发射的高吸收灯丝区分开来。

更新日期:2021-04-06
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