Radio Interferometric Calibration Using a Riemannian Manifold

TL;DR

This paper introduces a novel calibration method for radio interferometry that reformulates the problem on a Riemannian manifold, leading to faster convergence and lower computational costs compared to traditional Euclidean approaches.

Contribution

It presents a new Riemannian manifold-based formulation for radio interferometric calibration and demonstrates its efficiency over conventional Euclidean methods.

Findings

Faster convergence with the Riemannian trust-region method.

Reduced computational cost in calibration process.

Improved calibration accuracy demonstrated.

Abstract

In order to cope with the increased data volumes generated by modern radio interferometers such as LOFAR (Low Frequency Array) or SKA (Square Kilometre Array), fast and efficient calibration algorithms are essential. Traditional radio interferometric calibration is performed using nonlinear optimization techniques such as the Levenberg-Marquardt algorithm in Euclidean space. In this paper, we reformulate radio interferometric calibration as a nonlinear optimization problem on a Riemannian manifold. The reformulated calibration problem is solved using the Riemannian trust-region method. We show that calibration on a Riemannian manifold has faster convergence with reduced computational cost compared to conventional calibration in Euclidean space.