Optimizing Global Coronal Magnetic Field Models Using Image-Based Constraints

TL;DR

This paper introduces a new method to enhance global coronal magnetic field models by integrating morphological constraints from coronal images, aiming to improve accuracy despite measurement challenges.

Contribution

The paper presents a novel approach to incorporate image-based constraints into coronal magnetic field modeling, advancing the accuracy of these models.

Findings

Initial tests show improved model accuracy

Method effectively integrates image-derived constraints

Potential applications in space weather prediction

Abstract

The coronal magnetic field directly or indirectly affects a majority of the phenomena studied in space physics. It provides energy for coronal heating, controls the release of coronal mass ejections (CMEs), and drives heliospheric and magnetospheric activity, yet the coronal magnetic field itself has proven difficult to measure. This difficulty has prompted a decades-long effort to develop accurate, timely, models of the field - an effort that continues today. We have developed a method for improving global coronal magnetic field models by incorporating the type of morphological constraints which could be derived from coronal images. Here we report promising initial tests of this approach on two theoretical problems, and discuss opportunities for application.