Nurgaliev, Daniyar RashidovichMcDonald, M.Benson, B. A.Miller, E. D.Stubbs, ChristopherViklinin, Alexey2014-02-282013Nurgaliev, D., M. McDonald, B. A. Benson, E. D. Miller, C. W. Stubbs, and A. Vikhlinin. 2013. “A Robust Quantification of Galaxy Cluster Morphology Using Asymmetry and Central Concentration.” The Astrophysical Journal 779 (2) (December 20): 112. doi:10.1088/0004-637x/779/2/112. http://dx.doi.org/10.1088/0004-637X/779/2/112.0004-637Xhttp://nrs.harvard.edu/urn-3:HUL.InstRepos:11829404We 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.en-USJournal Article2014-01-17D Nurgaliev, M McDonald, BA Benson, ED Miller, CW Stubbs, A Vikhlinin2014-02-2810.1088/0004-637x/779/2/112