Preprocess the Photospheric Vector Magnetograms for NLFFF Extrapolation using a Potential Field Model and an Optimization Method

TL;DR

This paper introduces a new magnetogram preprocessing method that separates potential and non-potential magnetic field components, effectively reducing force and noise in photospheric data to improve NLFFF extrapolation accuracy.

Contribution

A novel preprocessing code based on magnetic field splitting and tailored for the CESE-MHD-NLFFF method, enhancing data quality for coronal magnetic field modeling.

Findings

Efficient removal of force and noise from magnetograms.

Improved accuracy of NLFFF extrapolation using the new preprocessing.

Successful application to SDO/HMI data.

Abstract

Numerical reconstruction/extrapolation of coronal nonlinear force-free magnetic field (NLFFF) usually takes the photospheric vector magnetogram as input at the bottom boundary. Magnetic field observed at the photosphere, however, contains force which is in conflict with the fundamental assumption of the force-free model and measurement noise which is unfavorable for practical computation. Preprocessing of the raw magnetogram has been proposed by Wiegelmann, Inhester, and Sakurai (2006) to remove the force and noise for providing better input for NLFFF modeling. In this paper we develop a new code of magnetogram preprocessing which is consistent with our extrapolation method CESE-MHD-NLFFF (Jiang, Feng, and Xiang, 2012; Jiang and Feng, 2012). Basing on a magnetic-splitting rule that a magnetic field can be split into a potential field part and a non-potential part, we split the magnetogram and deal with the two parts separately. Preprocessing of the magnetogram's potential part is based on a numerical potential field model, and the non-potential part is preprocessed using the similar optimization method of Wiegelmann et al (2006). The code is applied to the SDO/HMI data and results show that the method can remove efficiently the force and noise and improve the quality of extrapolation.