Stereoscopic coronal magnetic field modeling

• Applicant: Dr. Thomas Wiegelmann
• Subject Area: Astrophysics and Astronomy
• Term: from 2017 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 342658497

• Final Report Year: 2022

Final Report Abstract

Within this project we further developed and applied codes for modelling the solar corona. These codes have been based on spherical and Cartesian nonlinear force-free extrapolation codes, which have been developed in our group earlier. An innovative approach done in this project was to incorporate machine learning techniques into the stereoscopic magnetic field modelling. The new method allows to derive the 3D-structure of coronal images from EUV-images taken from only one viewpoint. Another new code development done in this project was to incorporate plasma forces and the solar wind flow into a new stationary MHD code. This stationary MHD-model allows to extend global corona models further outwards into the heliosphere, where plasma forces and effects of the solar wind become important. This work confirmed also, that in the inner corona up to about 2 solar radii the magnetic field derived from nonlinear force-free and stationary MHD models hardly differ, whereas further outwards the solar wind stretches and opens the magnetic field. One important application of our models was the first study of the evolution of the global coronal magnetic field during an entire solar cycle. While usually the sunspot number is used as a measure for the activity of the sun, we found that for the investigated cycle 24, the maximum solar activity in terms of emerging flux occurs about ten months after the peek of the sunspot numbers. A strong correlation was found between the free magnetic energy computed from our model and the observed flaring activity.

Publications

• An Optimization Principle for Computing Stationary MHD Equilibria with Solar Wind Flow, Sol. Phys, 2020, 295, 145
  Wiegelmann, T., Neukirch, T., Nickeler, D. H., and Chifu, I.
  (See online at https://doi.org/10.1007/s11207-020-01719-8)
• 3D Solar Coronal Loop Reconstructions with Machine Learning, ApJL, 2021, 910, L10
  Chifu, I. and Gafeira, R.
  (See online at https://doi.org/10.3847/2041-8213/abed53)
• Coronal Magnetic Field Modeling over Cycle 24, A&A, 2022
  Chifu, I., Inhester, B., and Wiegelmann, T.
  (See online at https://doi.org/10.1051/0004-6361/202038001)