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arXiv 2103.08887astro-ph.SR
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基于代理的太阳极端紫外辐射深度学习预测

Proxy-Based Prediction of Solar Extreme Ultraviolet Emission using Deep Learning

Anthony L. Pineci, Peter Sadowski, Eric Gaidos, Xudong Sun

摘要:

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都可以驱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,这些辐射中的大部分是不可观测的。这促使人们确定一种可以从地面容易地观察到的代用品。在这里,我们评估的吸收在近红外1083 nm的三线中性orthohelium作为代理的极紫外线(EUV)发射的30.4 nm线的He II和17.1 nm线的Fe IX从太阳。我们应用深度学习来建模非线性关系,训练和验证模型的历史上,太阳盘的同期图像中获得的三重He I线由地面SOLIS天文台和EUV由美国宇航局太阳动力学天文台。该模型是一个全卷积神经网络(FCN),它包含了空间信息,并考虑了球形太阳到二维图像的投影。使用标准化的目标值,结果表明,中位数像素的相对误差为20%,平均磁盘集成通量误差为7%,在一个保持了测试集。定性地说,该模型学习He I吸收和EUV发射之间的复杂空间相关性,其预测能力优于逐像素模型;它还可以区分活动区域和不会导致EUV发射的高吸收灯丝。

英文摘要:

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 non-linear 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 (FCN) that incorporates spatial information and accounts for the projection of the spherical Sun to 2-d images. Using normalized target values, results indicate a median pixel-wise 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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