http://arxiv.org/abs/2301.04688
Aims. 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. RML 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 robust in the presence of data noise. As such it shows considerable promise for computing differential emission measure profiles from datasets of discrete spectral lines.
P. Massa, A. Emslie, I. Hannah, et. al.
Fri, 13 Jan 23
47/72
Comments: N/A