Abstract
Several methods are used to evaluate, from observational data, the dynamical state of galaxy clusters. Among them, the morphological analysis of cluster images is well suited for this purpose. We report a new approach to the morphology, which consists in analytically modelling the images with a set of orthogonal functions, the Zernike polynomials (ZPs). We validated the method on mock high-resolution Compton parameter maps of synthetic galaxy clusters from The Three Hundred project. To classify the maps for their morphology we defined a single parameter, C, by combining the contribution of some ZPs in the modelling. We verify that C is linearly correlated with a combination of common morphological parameters and also with a proper 3D dynamicalstate indicator available for the synthetic clusters we used. We also show the early results of the Zernike modelling applied on Compton parameter maps of local clusters (z < 0:1) observed by the Planck satellite. At last, we report the preliminary results of this kind of morphological analysis on mock X-ray maps of The Three Hundred clusters.
Highlights
Classify galaxy clusters based on their dynamical state is crucial to correctly infer other physical properties of those systems
When fitting the mock y-maps as in eq 2, we verified that Zernike polynomials (ZPs) with, 0 have values negligible in case of regular distributions in the m cnm maps, while they increase when dealing with complex patterns involving e.g. asymmetries or substructures
We found that the X-ray maps are poor modelled with the low-order ZPs we used for the y-maps
Summary
Classify galaxy clusters based on their dynamical state is crucial to correctly infer other physical properties of those systems. It is well known that galaxy clusters are dynamically active systems and their physical state does not always reflect a condition of equilibrium. The analysis of the morphological appearance of multiwavelength images is a common approach used to classify clusters in di↵erent dynamical classes. Several morphological parameters can be defined based on characteristic features in the images and used in some combinations Several morphological parameters can be defined based on characteristic features in the images and used in some combinations (see e.g. [1], [2], [3] and references therein)
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