Benchmark Test of Differential Emission Measure Codes and Multi-Thermal Energies in Solar Active Regions

TL;DR

This study evaluates 11 different Differential Emission Measure (DEM) methods using synthetic data from multiple solar observatories to assess their accuracy in determining active region and flare properties.

Contribution

It provides a comprehensive benchmark comparison of various DEM codes, highlighting the most accurate methods for solar plasma diagnostics.

Findings

Averaged DEM methods recover temperature and emission measure within 20-40% accuracy.

AIA spatial synthesis, EVE+GOES, and EVE+RHESSI methods are the most precise.

Multi-thermal energy estimates are within 40% of true values.

Abstract

We compare the ability of 11 Differential Emission Measure (DEM) forward-fitting and inversion methods to constrain the properties of active regions and solar flares by simulating synthetic data using the instrumental response functions of SDO/AIA, SDO/EVE, RHESSI, and GOES/XRS. The codes include the single-Gaussian DEM, a bi-Gaussian DEM, a fixed-Gaussian DEM, a linear spline DEM, the spatial synthesis DEM, the Monte-Carlo Markov chain DEM, the regularized DEM inversion, the Hinode/XRT method, a polynomial spline DEM, an EVE+GOES, and an EVE+RHESSI method. Averaging the results from all 11 DEM methods, we find the following accuracies in the inversion of physical parameters: the EM-weighted temperature $T_w^{fit}/T_w^{sim}=0.9\pm0.1$, the peak emission measure $EM_p^{fit}/EM_p^{sim}=0.6\pm0.2$, the total emission measure $EM_t^{fit}/EM_t^{sim}=0.8\pm0.3$, and the multi-thermal energies $E_{th}^{fit}/EM_{th}^{sim}=1.2\pm0.4$. We find that the AIA spatial synthesis, the EVE+GOES, and the EVE+RHESSI method yield the most accurate results.