Data-driven MHD simulation of successive solar plasma eruptions

TL;DR

This paper presents a novel data-driven MHD simulation method that accurately reproduces successive solar plasma eruptions by inferring coronal magnetic fields from photospheric observations.

Contribution

The authors developed a new approach to simulate coronal magnetic field evolution using observational data and inverse induction, enabling realistic modeling of solar eruptions.

Findings

Successfully simulated successive eruptions observed in 2017.

Reproduced converging magnetic motions and flux rope formations.

Demonstrated the method's effectiveness in capturing eruption dynamics.

Abstract

Solar flares and plasma eruptions are sudden releases of magnetic energy stored in the plasma atmosphere. To understand the physical mechanisms governing their occurrences, three-dimensional magnetic fields from the photosphere up to the corona must be studied. The solar photospheric magnetic fields are observable, whereas the coronal magnetic fields cannot be measured. One method for inferring coronal magnetic fields is performing data-driven simulations, which involves time-series observational data of the photospheric magnetic fields with the bottom boundary of magnetohydrodynamic simulations. We developed a data-driven method in which temporal evolutions of the observational vector magnetic field can be reproduced at the bottom boundary in the simulation by introducing an inverted velocity field. This velocity field is obtained by inversely solving the induction equation and applying an appropriate gauge transformation. Using this method, we performed a data-driven simulation of successive small eruptions observed by the Solar Dynamics Observatory and the Solar Magnetic Activity Telescope in November 2017. The simulation well reproduced the converging motion between opposite-polarity magnetic patches, demonstrating successive formation and eruptions of helical flux ropes.