Modeling the Global Distribution of Solar Wind Parameters on the Source Surface Using Multiple Observations and the Artificial Neural Network Technique
Modeling the Global Distribution of Solar Wind Parameters on the Source Surface Using Multiple Observations and the Artificial Neural Network Technique
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利用多次观测和人工神经网络技术对源表面太阳风参数的全球分布进行建模
DOI：
10.1007/s11207-019-1496-5
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发表时间：
2019
期刊：
影响因子：
2.8
通讯作者：
Shen Fang
中科院分区：
文献类型：
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作者：
Yang Yi;Shen Fang
源表面磁场和其他等离子体参数的全局分布（我们将其设置为 2.5 个太阳半径）对于日冕和日光层建模非常重要。在本文中，我们介绍了一种新的数据驱动的自洽方法来获取不同参数的全局分布。磁和偏振亮度 () 观测结果分别用于导出源表面上的磁场和电子密度。然后，应用人工神经网络（ANN）机器学习技术来建立太阳风速度、磁场特性和电子密度之间的经验关系。该人工神经网络经过全球观测数据的训练，经验证比重建太阳风速度的 Wang-Sheeley-Arge (WSA) 模型更可靠，尤其是在高纬度地区。通过求解源表面上的简化一维 (1D) 磁流体动力学 (MHD) 方程组得出等离子体温度分布。使用本研究中的方法，我们可以基于磁和偏振亮度观测自洽地获得所有参数的全局分布。提出了来自不同太阳周期阶段的四个卡林顿旋转的建模结果来验证该方法。
The global distribution of magnetic field and other plasma parameters on the source surface, which we set at 2.5 solar radii, is important for coronal and heliospheric modeling. In this article, we introduce a new data-driven self-consistent method to obtain the global distribution of different parameters. The magnetic and polarized brightness () observations are used to derive the magnetic field and electron density on the source surface, respectively. Then, an artificial neural network (ANN) machine learning technique is applied to establish an empirical relation among the solar wind velocity, the magnetic field properties, and the electron density. The ANN is trained with global observational data, and is validated to be more reliable than the Wang–Sheeley–Arge (WSA) model for reconstructing the solar wind velocity, especially at high latitudes. The plasma temperature distribution is derived by solving a simplified one-dimensional (1D) magnetohydrodynamic (MHD) equation system on the source surface. Using the method in this study we can obtain the global distribution for all the parameters self-consistently based on magnetic and polarized brightness observations. The modeling results of four Carrington rotations from different solar cycle phases are presented to validate the method.