Robust Quantification of Galaxy Cluster Morphology Using Asymmetry and Central Concentration

Abstract

We present a novel quantitative scheme of cluster classification based on the morphological properties that are manifested in X-ray images. We use a conventional radial surface brightness concentration parameter (c_{SB}) as defined previously by others, and a new asymmetry parameter, which we define in this paper. Our asymmetry parameter, which we refer to as photon asymmetry ($A_{phot}), was developed as a robust substructure statistic for cluster observations with only a few thousand counts. To demonstrate that photon asymmetry exhibits better stability than currently popular power ratios and centroid shifts, we artificially degrade the X-ray image quality by: (a) adding extra background counts, (b) eliminating a fraction of the counts, (c) increasing the width of the smoothing kernel, and (d) simulating cluster observations at higher redshift. The asymmetry statistic presented here has a smaller statistical uncertainty than competing substructure parameters, allowing for low levels of substructure to be measured with confidence. A_{phot} is less sensitive to the total number of counts than competing substructure statistics, making it an ideal candidate for quantifying substructure in samples of distant clusters covering wide range of observational S/N. Additionally, we show that the asymmetry-concentration classification separates relaxed, cool core clusters from morphologically-disturbed mergers, in agreement with by-eye classifications. Our algorithms, freely available as Python scripts (https://github.com/ndaniyar/aphot) are completely automatic and can be used to rapidly classify galaxy cluster morphology for large numbers of clusters without human intervention.