Near real-time construction of Solar Coronal Parameters based on MAS simulation by Deep Learning

Magnetohydrodynamic (MHD) models provide a quantitative 3D distribution of the solar corona parameters (density, radial velocity, and temperature). However, this process is expensive and time-consuming. For this, we apply deep learning models to reproduce the 3D distribution of solar coronal parameters from 2D synoptic photospheric magnetic fields. We consider synoptic photospheric magnetic fields as an input to obtain 3D solar coronal parameters simulated by the MHD Algorithm outside a Sphere (MAS) from June 2010 to January 2023. Each parameter is individually trained using 150 deep learning models, corresponding to 150 solar radial distances ranging from 1 to 30 solar radii. Our study yields significant findings. Firstly, our model accurately reproduces 3D coronal parameter structures across the 1 to 30 solar radii range, demonstrating an average correlation coefficient value of approximately 0.96. Secondly, the 150 deep-learning models exhibit a remarkably shorter runtime (about 16 seconds for each parameter), with an NVIDIA Titan XP GPU, in comparison to the conventional MAS simulation time. As the MAS simulation is a regularization model, we may significantly reduce the simulation time by using our results as an initial magnetic configuration to obtain an equilibrium condition. In the future, we hope that the generated solar coronal parameters can be used for near real-time forecasting of heliospheric propagation of solar eruptions.