Estimation of Radio Interferometer Beam Shapes Using Riemannian Optimization

TL;DR

This paper introduces a Riemannian optimization method to estimate time-varying radio interferometer beam shapes directly from observational data, improving accuracy over traditional techniques.

Contribution

It presents a novel Riemannian optimization framework for beam shape estimation, addressing the challenge of modeling dynamic beams in radio interferometry.

Findings

Riemannian optimization outperforms unconstrained methods

The approach effectively models time-varying beams

Results demonstrate improved imaging accuracy

Abstract

The knowledge of receiver beam shapes is essential for accurate radio interferometric imaging. Traditionally, this information is obtained by holographic techniques or by numerical simulation. However, such methods are not feasible for an observation with time varying beams, such as the beams produced by a phased array radio interferometer. We propose the use of the observed data itself for the estimation of the beam shapes. We use the directional gains obtained along multiple sources across the sky for the construction of a time varying beam model. The construction of this model is an ill posed non linear optimization problem. Therefore, we propose to use Riemannian optimization, where we consider the constraints imposed as a manifold. We compare the performance of the proposed approach with traditional unconstrained optimization and give results to show the superiority of the proposed approach.