Sparse interferometric Stokes imaging under polarization constraint (Polarized SARA)

TL;DR

This paper introduces Polarized SARA, a novel convex optimization algorithm for sparse radio interferometric Stokes imaging that enforces physical polarization constraints and improves image quality over existing methods.

Contribution

It develops a new primal-dual algorithm incorporating polarization constraints and average sparsity priors for enhanced polarimetric imaging in radio interferometry.

Findings

Imposing polarization constraints improves image quality.

Average sparsity prior outperforms other priors in polarimetric imaging.

Method demonstrates superior results on simulated M87 observations.

Abstract

We develop a novel algorithm for sparse Stokes parameters imaging in radio interferometry under the polarization constraint. The latter is a physical non-linear relation between the Stokes parameters, imposing that the polarization intensity is a lower bound on the total intensity. To solve the joint inverse Stokes imaging problem including this bound, we leverage epigraphical projection techniques in convex optimization and design a primal-dual method offering a highly flexible and parallelizable structure. In addition, we propose to regularize each Stokes parameter map through an average sparsity prior in the context of a reweighted analysis approach (SARA). The resulting approach is dubbed Polarized SARA. We demonstrate on simulated observations of M87 with the Event Horizon Telescope that imposing the polarization constraint leads to superior image quality. The results also confirm that the performance of the average sparsity prior surpasses the alternative state-of-the-art priors for polarimetric imaging.