# Unified Radio Interferometric Calibration and Imaging with Joint Uncertainty Quantification

## TL;DR

This paper introduces a Bayesian algorithm that unifies radio interferometric calibration and imaging, providing both sky brightness estimates and joint uncertainty quantification, enhancing the reliability of radio astronomical data analysis.

## Contribution

The paper presents a novel Bayesian framework using information field theory and MGVI that jointly calibrates and images radio interferometric data with uncertainty estimation.

## Key findings

- Provides joint calibration and imaging with uncertainty quantification.
- Implemented as a practical algorithm for radio interferometry.
- Focuses on direction-independent antenna calibration.

## Abstract

The data reduction procedure for radio interferometers can be viewed as a combined calibration and imaging problem. We present an algorithm that unifies cross-calibration, self-calibration, and imaging. Being a Bayesian method, that algorithm does not only calculate an estimate of the sky brightness distribution, but also provides an estimate of the joint uncertainty which entails both the uncertainty of the calibration and the one of the actual observation. The algorithm is formulated in the language of information field theory and uses Metric Gaussian Variational Inference (MGVI) as the underlying statistical method. So far only direction-independent antenna-based calibration is considered. This restriction may be released in future work. An implementation of the algorithm is contributed as well.