Faceting for direction-dependent spectral deconvolution

TL;DR

This paper introduces DDFacet, a wide-band, wide-field spectral deconvolution framework for radio interferometry that accounts for direction-dependent effects using image plane faceting and advanced algorithms.

Contribution

It presents a novel wide-field, wide-band spectral deconvolution method incorporating direction-dependent effects and efficient computational techniques like baseline dependent averaging.

Findings

Implemented a flexible deconvolution framework (DDFacet)

Developed two spectral deconvolution algorithms based on hybrid matching pursuit and sub-space optimisation

Enhanced computational efficiency with baseline dependent averaging

Abstract

The new generation of radio interferometers is characterized by high sensitivity, wide fields of view and large fractional bandwidth. To synthesize the deepest images enabled by the high dynamic range of these instruments requires us to take into account the direction-dependent Jones matrices, while estimating the spectral properties of the sky in the imaging and deconvolution algorithms. In this paper we discuss and implement a wide-band wide-field spectral deconvolution framework (DDFacet) based on image plane faceting, that takes into account generic direction-dependent effects. Specifically, we present a wide-field co-planar faceting scheme, and discuss the various effects that need to be taken into account to solve for the deconvolution problem (image plane normalization, position-dependent PSF, etc). We discuss two wide-band spectral deconvolution algorithms based on hybrid matching pursuit and sub-space optimisation respectively. A few interesting technical features incorporated in our imager are discussed, including baseline dependent averaging, which has the effect of improving computing efficiency. The version of DDFacet presented here can account for any externally defined Jones matrices and/or beam patterns.