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

Massa, P., Emslie, A. G., Hannah, I. G. and Kontar, E. P. (2023) Robust construction of differential emission measure profiles using a regularized maximum likelihood method. Astronomy and Astrophysics, 672, A120. (doi: 10.1051/0004-6361/202345883)

Abstract

Context. 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 ξ(Τ), such that ξ(Τ) 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 ξ(T) profile from a set of EUV spectral line intensities observed at a pixel within a solar image. Aims. 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. Methods. 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. Results. 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. Conclusions. 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.

• Item Type: Articles
• Additional Information: PM and AGE were supported by NASA Kentucky under award number 80NSSC21M0362; IGH and EPK were supported by STFC consolidated grant ST/T000422/1.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Kontar, Professor Eduard and Hannah, Dr Iain
• Authors: Massa, P., Emslie, A. G., Hannah, I. G., and Kontar, E. P.
• College/School: College of Science and Engineering > School of Physics and Astronomy
• Journal Name: Astronomy and Astrophysics
• Publisher: EDP Sciences
• ISSN: 0004-6361
• ISSN (Online): 1432-0746
• Published Online: 12 April 2023
• Copyright Holders: Copyright © The Authors 2023
• First Published: First published in Astronomy and Astrophysics 672:A120
• Publisher Policy: Reproduced under a Creative Commons license

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
306515		PHAS A&A Group STFC Consolidated	Lyndsay Fletcher	Science and Technology Facilities Council (STFC)	ST/T000422/1	P&S - Physics & Astronomy