A ROBUST QUANTIFICATION OF GALAXY CLUSTER MORPHOLOGY USING ASYMMETRY AND CENTRAL CONCENTRATION

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 (1) adding extra background counts, (2) eliminating a fraction of the counts, (3) increasing the width of the smoothing kernel, and (4) 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 a wide range of observational signal-to-noise ratios. 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.