The adaptive-loop-gain adaptive-scale CLEAN deconvolution of radio interferometric images

TL;DR

This paper introduces Algas-Clean, an adaptive-loop-gain and scale deconvolution algorithm for radio interferometric images, improving accuracy and convergence over traditional fixed-parameter CLEAN methods.

Contribution

The paper presents a novel adaptive-loop-gain scheme integrated with scale adaptation, enhancing deconvolution performance in radio imaging.

Findings

More accurate image models produced

Faster convergence in deconvolution process

Improved handling of telescope PSF effects

Abstract

CLEAN algorithms are a class of deconvolution solvers which are widely used to remove the effect of the telescope Point Spread Function (PSF). Loop gain is one important parameter in CLEAN algorithms. Currently the parameter is fixed during deconvolution, which restricts the performance of CLEAN algorithms. In this paper, we propose a new deconvolution algorithm with an adaptive loop gain scheme, which is referred to as the adaptive-loop-gain adaptivescale CLEAN (Algas-Clean) algorithm. The test results show that the new algorithm can give a more accurate model with faster convergence.