Parameter optimization for surface flux transport models
T. Whitbread, A. R. Yeates, A. Mu\~noz-Jaramillo, G. J. D. Petrie

TL;DR
This study uses a genetic algorithm to optimize parameters in surface flux transport models for solar cycles, improving the accuracy of solar activity predictions by fitting observed data with different model configurations.
Contribution
It introduces a genetic algorithm-based method for parameter optimization in both 1D and 2D solar surface flux transport models, accounting for cycle-to-cycle variations.
Findings
Optimal parameters align with observational bounds.
2D model better fits observed flow profiles.
Cycle variations influence model parameters.
Abstract
Accurate prediction of solar activity calls for precise calibration of solar cycle models. Consequently we aim to find optimal parameters for models which describe the physical processes on the solar surface, which in turn act as proxies for what occurs in the interior and provide source terms for coronal models. We use a genetic algorithm to optimize surface flux transport models using National Solar Observatory (NSO) magnetogram data for Solar Cycle 23. This is applied to both a 1D model that inserts new magnetic flux in the form of idealized bipolar magnetic regions, and also to a 2D model that assimilates specific shapes of real active regions. The genetic algorithm searches for parameter sets (meridional flow speed and profile, supergranular diffusivity, initial magnetic field, and radial decay time) that produce the best fit between observed and simulated butterfly diagrams,…
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