Multi-Scale CLEAN: A comparison of its performance against classical CLEAN in galaxies using THINGS

TL;DR

This paper evaluates the Multi-Scale CLEAN algorithm's performance on galaxy data from THINGS, showing it mitigates classical CLEAN issues like the 'bowl' effect and improves cleaning down to noise levels.

Contribution

It provides a practical comparison demonstrating that Multi-Scale CLEAN significantly improves image quality over classical CLEAN in galaxy observations.

Findings

Multi-Scale CLEAN reduces the 'bowl' effect caused by missing short spacings.

It cleans down to noise levels without divergence issues.

Improves contrast for features like HI holes and anomalous gas structures.

Abstract

A practical evaluation of the Multi-Scale CLEAN algorithm is presented. The data used in the comparisons are taken from The HI Nearby Galaxy Survey (THINGS). The implementation of Multi-Scale CLEAN in the CASA software package is used, although comparisons are made against the very similar Multi-Resolution CLEAN algorithm implemented in AIPS. Both are compared against the classical CLEAN algorithm (as implemented in AIPS). The results of this comparison show that several of the well-known characteristics and issues of using classical CLEAN are significantly lessened (or eliminated completely) when using the Multi-Scale CLEAN algorithm. Importantly, Multi-Scale CLEAN reduces significantly the effects of the clean `bowl' caused by missing short-spacings, and the `pedestal' of low-level un-cleaned flux (which affects flux scales and resolution). Multi-Scale CLEAN can clean down to the noise level without the divergence suffered by classical CLEAN. We discuss practical applications of the added contrast provided by Multi-Scale CLEAN using two selected astronomical examples: HI holes in the interstellar medium and anomalous gas structures outside the main galactic disk.