Correlation Calibration: A Hybrid Calibration Technique for Radio Interferometric Arrays

TL;DR

CorrCal is a hybrid calibration method for radio interferometric arrays that combines sky-based and redundant calibration, offering robustness to modeling errors and computational efficiency for 21 cm cosmology data analysis.

Contribution

It introduces CorrCal, a novel covariance-based hybrid calibration technique that is computationally efficient and resilient to sky and instrument modeling errors.

Findings

Unbiased calibration errors below thermal noise thresholds.

Capable of evaluating likelihood and gradient in less than a second for 1,000-element arrays.

Robust performance across various modeling error scenarios.

Abstract

Calibrating out per-antenna signal chain effects is an essential step in analyzing radio interferometric data. For drift-scanning arrays, robustly calibrating the data is especially challenging due to the lack of the ability to track a calibration source. Consequently, calibration strategies for drift-scanning arrays are limited by our knowledge of the radio sky at large, as well as the direction-dependent instrument response. In the context of 21 cm cosmology, where small calibration errors can conspire to overwhelm the cosmological signal, it is therefore crucially important to develop calibration strategies that are capable of accurately calibrating the data in the presence of sky or instrument modeling errors. In this paper we present CorrCal, a covariance-based calibration strategy for redundant radio interferometric arrays. CorrCal is a hybrid calibration strategy that leverages the strengths of traditional sky-based calibration and redundant calibration in a computationally efficient framework that is fairly insensitive to modeling errors. We find that the calibration errors from CorrCal are unbiased and far below typical thermal noise thresholds across a wide range of modeling error scenarios. We show that CorrCal is computationally efficient: our implementation is capable of evaluating the likelihood and its gradient in less than a second for 1,000-element class arrays using just a single laptop core. Given CorrCal's computational efficiency and robustness to modeling errors, we anticipate that it will serve as a useful tool in the analysis of radio interferometric data from current and next-generation experiments targeting the cosmological 21 cm signal.