Radio Interferometric Calibration Using a Complex Student's t-distribution and Wirtinger Derivatives

TL;DR

This paper introduces a robust radio interferometric calibration algorithm based on Student's t-distribution and Wirtinger calculus, improving bias mitigation from incomplete sky models and interference, demonstrated on simulated and real data.

Contribution

It presents a novel calibration method using Student's t-distribution with Wirtinger derivatives, integrated into CubiCal, enhancing robustness over traditional Gaussian-based approaches.

Findings

Significant bias reduction in calibration results

Improved image dynamic range in real data applications

Outperforms conventional Gaussian likelihood-based solvers

Abstract

Radio interferometric gain calibration can be biased by incomplete sky models and radio frequency interference, resulting in calibration artefacts that can restrict the dynamic range of the resulting images. It has been suggested that calibration algorithms employing heavy-tailed likelihood functions are less susceptible to this due to their robustness against outliers in the data. We present an algorithm based on a Student's t-distribution which leverages the framework of complex optimisation and Wirtinger calculus for efficient and robust interferometric gain calibration. We integrate this algorithm as an option in the newly released calibration software package, CubiCal. We demonstrate that the algorithm can mitigate some of the biases introduced by incomplete sky models and radio frequency interference by applying it to both simulated and real data. Our results show significant improvements compared to a conventional least-squares solver which assumes a Gaussian likelihood function. Furthermore, we provide some insight into why the algorithm outperforms the conventional solver, and discuss specific scenarios (for both direction-independent and direction-dependent self-calibration) where this is expected to be the case.