Modeling the Global Distribution of Solar Wind Parameters on the Source Surface Using Multiple Observations and the Artificial Neural Network Technique

Abstract

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 ( $pB$ ) 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.