Improving Coronal Magnetic Field Models Using Image Optimization

Abstract

Abstract We have reported previously on our development and testing of a new method for using coronal images to improve coronal magnetic field models. In this technique, which we call image-optimization, coronal magnetic field models are extrapolated from synoptic photospheric magnetograms. The resulting models are then compared to morphological constraints derived from images of the solar corona, and the photospheric magnetograms are perturbed iteratively via an optimization algorithm to achieve optimal agreement with the image-based constraints. Here we present results from the first application of this technique using Mauna Loa Solar Observatory K-Coronagraph images and Global Oscillation Network Group synoptic magnetograms to create optimized models for two time periods, 2014 November 16–29 and 2016 May 16–29. We find that for both time periods the optimization algorithm converges well and results in better agreement between the model and the images, relatively small changes to the synoptic magnetogram, and an overall increase in the amount of open magnetic flux.