Distribution of the H\"ogbom CLEAN Algorithm Using Tiled Images with Feedback

TL;DR

This paper introduces a feedback-based tiled approach to the H"ogbom CLEAN algorithm, enabling faster deconvolution of large radio astronomy datasets with minimal accuracy loss.

Contribution

It presents a novel feedback mechanism for tiled CLEAN deconvolution, improving speed and accuracy in processing large interferometric images.

Findings

Achieved up to 10.66 times faster deconvolution.

Reconstructed sources within ±0.1 Jy of standard method.

Effective handling of faint sources in tiled images.

Abstract

Data sizes for next generation radio telescopes, such as the Square Kilometre Array (SKA), are far above that of their predecessors. The CLEAN algorithm was originally developed by H\"ogbom [1974], long before such data sizes were thought possible and is still the most popular tool used for deconvolution in interferometric imaging. In order to facilitate these new large data sizes and reduce computation time a distributed approach to the algorithm has been investigated. The serial nature of the CLEAN algorithm, due to its matching pursuit design, makes this challenging. Splitting the image into a number of tiles which can be individually deconvolved has been investigated, but this creates discontinuities in the deconvolved image and makes it difficult to deconvolve faint sources in the presence of a point spread function associated with bright sources in other tiles. A method of feedback between each of the tiles has been developed to deal with these problems. This new approach has been tested on a simulated dataset containing multiple point sources of known intensity. When compared to a standard H\"ogbom deconvolution the tiled feedback version produced a reconstructed image, containing sources up to 2.1 Jy, which agreed to between -0.1 Jy and +0.04 Jy of the standard method across the whole deconvolved image at a speed up to 10.66 times faster.