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arXiv · 2305.16080

Effects of optimisation parameters on data-driven magnetofrictional modelling of active regions

A. Kumari·D. J. Price·F. Daei·J. Pomoell·E. K. J. Kilpua
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Abstract

Data-driven time-dependent magnetofrictional modelling (TMFM) of active region magnetic fields has been proven to be a useful tool to study the corona. The input to the model is the photospheric electric field that is inverted from a time series of the photospheric magnetic field. Constraining the complete electric field, that is, including the non-inductive component, is critical for capturing the eruption dynamics. We present a detailed study of the effects of optimisation of the non-inductive electric field on the TMFM of AR12473. We aim to study the effects of varying the non-inductive electric field on the data-driven coronal simulations, for two alternative parametrisations. By varying parameters controlling the strength of the non-inductive electric field, we wish to explore the changes in flux rope formation and their early evolution and other parameters, for instance, axial flux and magnetic field magnitude.The non-inductive electric field component in the photosphere is critical for energising and introducing twist to the coronal magnetic field, thereby allowing unstable configurations to be formed. We estimated this component using an approach based on optimising the injection of magnetic energy. However, the flux rope formation, evolution and eruption time varies depending on the values of the optimisation parameters. The flux rope is formed and has overall similar evolution and properties with a large range of non-inductive electric fields needed to determine the non-inductive electric field component that is critical for energising and introducing twist to the coronal magnetic field. This study shows that irrespective of non-inductive electric field values, flux ropes are formed and erupted, which indicates that data-driven TMFM can be used to estimate flux rope properties early in their evolution without employing a lengthy optimisation process.

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A. Kumari, D. J. Price, F. Daei, J. Pomoell, E. K. J. Kilpua. 2023-05-25. Effects of optimisation parameters on data-driven magnetofrictional modelling of active regions. https://doi.org/10.1051/0004-6361%2F202244650

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