Expectation maximization for hard X-ray count modulation profiles

¹ Dipartimento di MatematicaUniversità di Genova, via Dodecaneso 35, 16146 Genova, Italy
e-mail: piana@dima.unige.it
² Catholic University and Solar Physics Laboratory, Goddard Space Flight Center, code 671, Greenbelt, MD 20771, USA
³ CNR – SPIN, via Dodecaneso 33, 16146 Genova, Italy

Received: 14 February 2013
Accepted: 12 March 2013

Abstract

Context. 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.

Aims. Our goal is to demonstrate that a statistical iterative method classically applied to the image deconvolution problem is very effective when utilized to analyze count modulation profiles in solar hard X-ray imaging based on rotating modulation collimators.

Methods. The algorithm described in this paper solves the maximum likelihood problem iteratively and encodes 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).

Results. 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.

Conclusions. 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.