Correcting direction-dependent gains in the deconvolution of radio interferometric images

TL;DR

This paper introduces a new imaging algorithm that corrects direction-dependent gains in radio interferometric images, significantly improving dynamic range and reducing systematic errors in high-sensitivity, full-polarization observations.

Contribution

The authors develop a general framework and an iterative deconvolution algorithm that effectively corrects for direction-dependent errors caused by antenna patterns and pointing inaccuracies.

Findings

Algorithm reduces systematic errors by an order of magnitude.

Simulations show correction of pointing errors and antenna pattern asymmetries.

Application to VLA data improves image quality significantly.

Abstract

Astronomical imaging using aperture synthesis telescopes requires deconvolution of the point spread function as well as calibration of instrumental and atmospheric effects. In general, such effects are time-variable and vary across the field of view as well, resulting in direction-dependent (DD), time-varying gains. Most existing imaging and calibration algorithms assume that the corruptions are direction independent, preventing even moderate dynamic range full-beam, full-Stokes imaging. We present a general framework for imaging algorithms which incorporate DD errors. We describe as well an iterative deconvolution algorithm that corrects known DD errors due to the antenna power patterns and pointing errors for high dynamic range full-beam polarimetric imaging. Using simulations we demonstrate that errors due to realistic primary beams as well as antenna pointing errors will limit the dynamic range of upcoming higher sensitivity instruments and that our new algorithm can be used to correct for such errors. We have applied this algorithm to VLA 1.4 GHz observations of a field that contains two ``4C'' sources and have obtained Stokes-I and -V images with systematic errors that are one order of magnitude lower than those obtained with conventional imaging tools. Our simulations show that on data with no other calibration errors, the algorithm corrects pointing errors as well as errors due to known asymmetries in the antenna pattern.