A Next-Generation Model of the Corona and Solar Wind

Linker, J. A.

doi:10.21236/ada563658

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…CORHEL delivers solutions to the community via Predictive Science Inc. (PSI). The PSI coronal code MAS solves timedependent MHD equations until a steady state is reached, with the boundary condition defined by a magnetic map (Linker 2011). For this study, we collected the solar coronal parameter data of 169 Carrington rotations (CRs) from 2010 June to 2023 February for training and testing.…”

Section: Datamentioning

confidence: 99%

“…In Figure 6, the results at 21.30 solar radii demonstrate the consistent structure of parameters near the heliospheric regions. This consistency is particularly crucial because the interface between the coronal and heliospheric regions is typically situated in the supercriticalflow region, which is near 21.5 solar radii or 0.1 au (Linker 2011).…”

Section: Qualitative Comparisonmentioning

confidence: 99%

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Near-real-time 3D Reconstruction of the Solar Coronal Parameters Based on the Magnetohydrodynamic Algorithm outside a Sphere Using Deep Learning

Rahman,

Jeong,

Siddique

et al. 2024

ApJS

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For the first time, we generate solar coronal parameters (density, magnetic field, radial velocity, and temperature) on a near-real-time basis by deep learning. For this, we apply the Pix2PixCC deep-learning model to three-dimensional (3D) distributions of these parameters: synoptic maps of the photospheric magnetic field as an input and the magnetohydrodynamic algorithm outside a sphere (MAS) results as an output. To generate the 3D structure of the solar coronal parameters from 1 to 30 solar radii, we train and evaluate 152 distinct deep-learning models. For each parameter, we consider the data of 169 Carrington rotations from 2010 June to 2023 February: 132 for training and 37 for testing. The key findings of our study are as follows: First, our deep-learning models successfully reconstruct the 3D distributions of coronal parameters from 1 to 30 solar radii with an average correlation coefficient of 0.98. Second, during the solar active and quiet periods, the AI-generated data exhibits consistency with the target MAS simulation data. Third, our deep-learning models for each parameter took a remarkably short time (about 16 s for each parameter) to generate the results with an NVIDIA Titan XP GPU. As the MAS simulation is a regularization model, we may significantly reduce the simulation time by using our results as an initial configuration to obtain an equilibrium condition. We hope that the generated 3D solar coronal parameters can be used for the near-real-time forecasting of heliospheric propagation of solar eruptions.

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Section: Datamentioning

confidence: 99%

Section: Qualitative Comparisonmentioning

confidence: 99%