  We consider imaging of solar flares from NASA RHESSI data as a parametric
imaging problem, where flares are represented as a finite collection of
geometric shapes. We set up a Bayesian model in which the number of objects
forming the image is a priori unknown, as well as their shapes. We use a
Sequential Monte Carlo algorithm to explore the corresponding posterior
distribution. We apply the method to synthetic and experimental data, largely
known in the RHESSI community. The method reconstructs improved images of solar
flares, with the additional advantage of providing uncertainty quantification
of the estimated parameters.
