Genetic Algorithm for Inferring Model Parameters for Flux Transport Dynamo Simulation

TL;DR

This paper introduces a genetic algorithm-based method to infer free parameters in solar dynamo models, improving the simulation's match to historical solar cycle data and enhancing understanding of solar interior dynamics.

Contribution

A novel genetic algorithm approach for inferring free parameters in flux transport dynamo models using observational solar cycle data.

Findings

Successfully reproduced solar cycle observations from 1723 to 2024

Demonstrated qualitative and quantitative agreement with observed data

Applicable to various historical and isotope-based solar data

Abstract

The Sun exhibits an 11-year cyclic variation, maintained by dynamo action in the solar interior. Mean-field flux transport dynamo models have successfully reproduced most of the features observed in solar cycles, while the model includes many free parameters, such as the speed of the meridional flow and the amplitude of the poloidal field generation. Inferring these free parameters is on demand because they correspond to the solar interior condition. We suggest a novel method for inferring the free parameters using a genetic algorithm. At each generation, we evaluate the fitness of our simulation against the observational data and optimize the parameters. We apply our method to the observed solar cycle data from 1723 to 2024 and successfully reproduce the observations from both qualitative and quantitative perspectives. We expect our method to be applicable to sunspot numbers, even those obtained from isotope data and historical documents, in the future, to better understand past solar interior dynamics.