Sparse Bayesian Imaging of Solar Flares
For the solar physics community, this offers improved flare imaging with uncertainty quantification, though it is an incremental application of existing Bayesian methods to a specific domain.
The paper addresses imaging of solar flares from RHESSI data using a Bayesian model with unknown number and shapes of geometric objects, and applies a Sequential Monte Carlo algorithm. The method reconstructs improved images and provides uncertainty quantification.
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.