Robust construction of differential emission measure profiles using a regularized maximum likelihood method

TL;DR

This paper introduces a regularized maximum likelihood algorithm for reconstructing differential emission measure profiles from EUV spectral data, improving robustness, positivity, and accuracy in the ill-posed inverse problem.

Contribution

The paper develops a novel RML method that guarantees positive, accurate, and robust differential emission measure profiles from EUV data, outperforming existing algorithms.

Findings

The RML method is mathematically rigorous and computationally efficient.

It produces more accurate and robust DEM profiles compared to other algorithms.

The method performs well on both simulated and real solar data.

Abstract

Extreme-ultraviolet (EUV) observations provide considerable insight into evolving physical conditions in the active solar atmosphere. For a prescribed density and temperature structure, it is straightforward to construct the corresponding differential emission measure profile $\xi(T)$, such that $\xi(T) \, dT$ is proportional to the emissivity from plasma in the temperature range $[T, T + dT]$. Here we study the inverse problem of obtaining a valid $\xi(T)$ profile from a set of EUV spectral line intensities observed at a pixel within a solar image. Our goal is to introduce and develop a regularized maximum likelihood (RML) algorithm designed to address the mathematically ill-posed problem of constructing differential emission measure profiles from a discrete set of EUV intensities in specified wavelength bands, specifically those observed by the Atmospheric Imaging Assembly (AIA) on the NASA Solar Dynamics Observatory. The RML method combines features of maximum likelihood and regularized approaches used by other authors. It is also guaranteed to produce a positive definite differential emission measure profile. We evaluate and compare the effectiveness of the method against other published algorithms, using both simulated data generated from parametric differential emission profile forms, and AIA data from a solar eruptive event on 2010 November 3. Similarities and differences between the differential emission measure profiles and maps reconstructed by the various algorithms are discussed. The RML inversion method is mathematically rigorous, computationally efficient, and produces acceptable measures of performance in the following three key areas: fidelity to the data, accuracy in the reconstruction, and robustness in the presence of data noise. As such, it shows considerable promise for computing differential emission measure profiles from datasets of discrete spectral lines.