Radio Interferometric Calibration via Ordered-Subsets Algorithms: OS-LS and OS-SAGE calibrations

TL;DR

This paper introduces accelerated radio interferometric calibration methods, OS-LS and OS-SAGE, combining ordered-subsets with existing algorithms to improve convergence speed and accuracy in simulated observations.

Contribution

The paper presents novel OS-LS and OS-SAGE calibration algorithms that significantly enhance convergence speed and accuracy over traditional methods in radio interferometry.

Findings

OS methods accelerate convergence nearly as well as non-OS methods.

OS-SAGE outperforms OS-LS in accuracy and computational efficiency.

Both methods show higher convergence rates in simulations.

Abstract

The main objective of this work is to accelerate the Maximum-Likelihood (ML) estimation procedure in radio interferometric calibration. We introduce the OS-LS and the OS-SAGE radio interferometric calibration methods, as a combination of the Ordered-Subsets (OS) method with the Least-Squares (LS) and Space Alternating Generalized Expectation maximization (SAGE) calibration techniques, respectively. The OS algorithm speeds up the ML estimation and achieves nearly the same level of accuracy of solutions as the one obtained by the non-OS methods. We apply the OS-LS and OS-SAGE calibration methods to simulated observations and show that these methods have a much higher convergence rate relative to the conventional LS and SAGE techniques. Moreover, the obtained results show that the OS-SAGE calibration technique has a superior performance compared to the OS-LS calibration method in the sense of achieving more accurate results while having significantly less computational cost.