Sparse Bayesian Imaging of Solar Flares

TL;DR

This paper presents a Bayesian imaging method for solar flares using RHESSI data, which models flares as geometric shapes, estimates their number and shapes, and quantifies uncertainty in the reconstructions.

Contribution

It introduces a Bayesian parametric imaging approach with a Sequential Monte Carlo algorithm that estimates flare shapes, counts, and uncertainties from RHESSI data.

Findings

Improved solar flare images with uncertainty quantification.

Effective application to synthetic and real RHESSI data.

Advancement in Bayesian modeling for solar imaging.

Abstract

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.