Evaluating Solar Convection Velocity Using Machine Learning and Numerical Simulation

Abstract

A method for evaluating the plasma motion on the solar surface is developed by a combination of numerical simulation and machine learning. On the solar surface, we can observe the thermal convection of plasma fluid. Since thermal convection relates to several phenomena such as magnetic field generation and coronal heating, the estimation of convection velocity is essential. While we can evaluate the line-of-sight velocity with the Doppler effect, the horizontal velocity field cannot be directly obtained. On the other hand, magnetohydrodynamic numerical simulations have been developed and can reproduce solar convection. We can get precise quantities such as velocities and magnetic fields on the solar surface in the simulations. The horizontal velocity, which is challenging to observe, is also obtained in the numerical simulations. Here, we construct a neural network to evaluate the horizontal velocity field on the solar surface with the observable quantities such as radiation intensity and the corresponding horizontal velocity field obtained from the simulation and applying them to the observation. The correlation coefficient between the horizontal velocities obtained by the neural network and the simulated data is 0.84 only with radiative intensity and 0.90 with radiative intensity, the line-of-sight velocity, and the line-of-sight-magnetic field. Even when we apply the network to the observation data, the correlation coefficient of 0.6 is kept.