A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

TL;DR

This paper introduces VIS_CS, a novel compressed sensing algorithm for reconstructing solar flare X-ray images from Fourier data, offering accurate, robust, and parameter-free results suitable for quick imaging and spectroscopy analysis.

Contribution

The paper presents a new compressed sensing-based algorithm, VIS_CS, that improves solar flare X-ray image reconstruction without needing source-specific tuning.

Findings

VIS_CS achieves accurate photometry and morphology in reconstructions.

The algorithm performs well on synthetic and real RHESSI data.

VIS_CS is suitable for rapid, large-scale imaging tasks.

Abstract

One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new Compressed Sensing-based algorithm named VIS_CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) satellite and compare its performance with existing algorithms. VIS_CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for generation of quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.