Optimization of Photospheric Electric Field Estimates for Accurate Retrieval of Total Magnetic Energy Injection (1712.05757v1)

Abstract: Estimates of the photospheric magnetic, electric and plasma velocity fields are essential for studying the dynamics of the solar atmosphere, for example through the derivative quantities of Poynting and relative helicity flux and by using of the fields to obtain the lower boundary condition for data-driven coronal simulations. In this paper we study the performance of a data processing and electric field inversion approach that requires only high-resolution and high-cadence line-of-sight or vector magnetograms -- which we obtain from Helioseismic and Magnetic Imager (HMI) onboard Solar Dynamics Observatory (SDO). The approach does not require any photospheric velocity estimates, and the lacking velocity information is compensated using ad hoc assumptions. We show that the free parameters of these assumptions can be optimized to reproduce the time evolution of the total magnetic energy injection through the photosphere in NOAA AR 11158, when compared to the recent estimates for this active region. However, we find that the relative magnetic helicity injection is reproduced poorly reaching at best a modest underestimation. We discuss also the effect of some of the data processing details on the results, including the masking of the noise-dominated pixels and the tracking method of the active region, both of which have not received much attention in the literature so far. In most cases the effect of these details is small, but when the optimization of the free parameters of the ad hoc assumptions is considered a consistent use of the noise mask is required. The results found in this paper imply that the data processing and electric field inversion approach that uses only the photospheric magnetic field information offers a flexible and straightforward way to obtain photospheric magnetic and electric field estimates suitable for practical applications such as coronal modeling studies.