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We suggest a method that evaluates the horizontal velocity in the solarphotosphere with easily observable values using a combination of neural networkand radiative magnetohydrodynamics simulations. All three-component velocitiesof thermal convection on the solar surface have important roles in generatingwaves in the upper atmosphere. However, the velocity perpendicular to the lineof sight (LoS) is difficult to observe. To deal with this problem, the localcorrelation tracking (LCT) method, which employs the difference between twoimages, has been widely used, but LCT has several disadvantages. We develop amethod that evaluates the horizontal velocity from a snapshot of the intensityand the LoS velocity with a neural network. We use data from numericalsimulations for training the neural network. While two consecutive intensityimages are required for LCT, our network needs just one intensity image at onlya specific moment for input. From these input array, our network outputs asame-size array of two-component velocity field. With only the intensity data,the network achieves a high correlation coefficient between the simulated andevaluated velocities of 0.83. In addition, the network performance can beimproved when we add LoS velocity for input, enabling achieving a correlationcoefficient of 0.90. Our method is also applied to observed data.

Solar horizontal flow evaluation using neural network and numerical simulation with snapshot data