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

• Main Authors: Nurgaliev, D. (Author), Benson, Bradford A. (Author), Stubbs, C. W. (Author), Vikhlinin, A. (Author), McDonald, Michael A. (Contributor), Miller, Eric D (Author)
• Other Authors: MIT Kavli Institute for Astrophysics and Space Research (Contributor), Miller, Eric D. (Contributor)
• Format: Article
• Language: English
• Published: IOP Publishing, 2015-02-03T16:54:16Z.
• Subjects: Article
• Online Access: Get fulltext

 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 [subscript 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 [subscript 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 [subscript 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.