Expectation Maximization for Hard X-ray Count Modulation Profiles

TL;DR

This paper demonstrates that an Expectation Maximization algorithm effectively reconstructs solar hard X-ray images from RHESSI data, offering advantages in accuracy and computational efficiency over traditional methods.

Contribution

The paper adapts the EM algorithm for image reconstruction from count modulation profiles in solar X-ray imaging, introducing a reliable stopping rule for regularization.

Findings

EM achieves comparable accuracy to Pixon with less computation.

EM outperforms CLEAN in measurement fidelity.

The stopping rule effectively regularizes the reconstruction.

Abstract

This paper is concerned with the image reconstruction problem when the measured data are solar hard X-ray modulation profiles obtained from the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI)} instrument. Our goal is to demonstrate that a statistical iterative method classically applied to the image deconvolution problem is very effective when utilized for the analysis of count modulation profiles in solar hard X-ray imaging based on Rotating Modulation Collimators. The algorithm described in this paper solves the maximum likelihood problem iteratively and encoding a positivity constraint into the iterative optimization scheme. The result is therefore a classical Expectation Maximization method this time applied not to an image deconvolution problem but to image reconstruction from count modulation profiles. The technical reason that makes our implementation particularly effective in this application is the use of a very reliable stopping rule which is able to regularize the solution providing, at the same time, a very satisfactory Cash-statistic (C-statistic). The method is applied to both reproduce synthetic flaring configurations and reconstruct images from experimental data corresponding to three real events. In this second case, the performance of Expectation Maximization, when compared to Pixon image reconstruction, shows a comparable accuracy and a notably reduced computational burden; when compared to CLEAN, shows a better fidelity with respect to the measurements with a comparable computational effectiveness. If optimally stopped, Expectation Maximization represents a very reliable method for image reconstruction in the RHESSI context when count modulation profiles are used as input data.