Effects of optimisation parameters on data-driven magnetofrictional modelling od AR12473
Date:
The solar magnetic field plays an essential role in the formation, evolution, and dynamics of large-scale eruptive structures in the corona. Estimation of the coronal magnetic field, the ultimate driver of space weather, particularly in the ‘low’ and ‘middle’ corona, is presently limited due to practical difficulties. Data-driven time-dependent magneticfrictional 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, i.e., 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 TMFM of AR12473. We study the effects of these optimisation parameters on the data-driven coronal simulations. By varying the free optimisation parameters, we explore the changes in flux rope formation and their early evolution and other parameters, e.g., axial flux and magnetic field magnitude. We used the high temporal and spatial resolution cadence vector magnetograms from the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). 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 estimate this component using an approach based on optimising the injection of magnetic energy. Our simulations show that flux ropes were formed in all of the simulations except the lower values of these parameters. However, the flux rope formation, evolution and eruption time varies depending upon the values of the optimisation parameters. This study shows that irrespective of ad hoc free parameters 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 needing to employ a lengthy optimisation process.