# Compressed sensing and Sequential Monte Carlo for solar hard X-ray imaging

## TL;DR

This paper introduces two inversion methods combining compressed sensing and Sequential Monte Carlo techniques for reconstructing solar hard X-ray images, validated on real and synthetic data from RHESSI and STIX instruments.

## Contribution

The paper presents novel inversion algorithms that integrate compressed sensing with Sequential Monte Carlo methods for improved solar X-ray imaging.

## Key findings

- Methods successfully reconstruct solar X-ray images from RHESSI data.
- Algorithms perform well on synthetic STIX data.
- Enhanced image quality compared to traditional techniques.

## Abstract

We describe two inversion methods for the reconstruction of hard X-ray solar images. The methods are tested against experimental visibilities recorded by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) and synthetic visibilities based on the design of the Spectrometer/Telescope for Imaging X-rays (STIX).