Studying the properties of galaxy cluster morphology estimators

X-ray observations of galaxy clusters reveal a large range of morphologies with various degrees of disturbance, showing that the assumptions of hydrostatic equilibrium and spherical shape which are used to determine the cluster mass from X-ray data are not always satisfied. It is therefore important for the understanding of cluster properties as well as for cosmological applications to detect and quantify substructure in X-ray images of galaxy clusters. Two promising methods to do so are power ratios and center shifts. Since these estimators can be heavily affected by Poisson noise and X-ray background, we performed an extensive analysis of their statistical properties using a large sample of simulated X-ray observations of clusters from hydrodynamical simulations. We quantify the measurement bias and error in detail and give ranges where morphological analysis is feasible. A new, computationally fast method to correct for the Poisson bias and the X-ray background contribution in power ratio and center shift measurements is presented and tested for typical XMM-Newton observational data sets. We studied the morphology of 121 simulated cluster images and establish structure boundaries to divide samples into relaxed, mildly disturbed and disturbed clusters. In addition, we present a new morphology estimator - the peak of the 0.3-1 r500 P3/P0 profile to better identify merging clusters. The analysis methods were applied to a sample of 80 galaxy clusters observed with XMM-Newton. We give structure parameters (P3/P0 in r500, w and P3/P0_max) for all 80 observed clusters. Using our definition of the P3/P0 (w) substructure boundary, we find 41% (47%) of our observed clusters to be disturbed.Comment: Replaced to match version published in A&A, Eq. 1 correcte

Deep Over-sampling Framework for Classifying Imbalanced Data

Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose Deep Over-sampling (DOS), a framework for extending the synthetic over-sampling method to exploit the deep feature space acquired by a convolutional neural network (CNN). Its key feature is an explicit, supervised representation learning, for which the training data presents each raw input sample with a synthetic embedding target in the deep feature space, which is sampled from the linear subspace of in-class neighbors. We implement an iterative process of training the CNN and updating the targets, which induces smaller in-class variance among the embeddings, to increase the discriminative power of the deep representation. We present an empirical study using public benchmarks, which shows that the DOS framework not only counteracts class imbalance better than the existing method, but also improves the performance of the CNN in the standard, balanced settings

Local structure of Liquid-Vapour Interfaces

The structure of a simple liquid may be characterised in terms of ground state clusters of small numbers of atoms of that same liquid. Here we use this sensitive structural probe to consider the effect of a liquid-vapour interface upon the liquid structure. At higher temperatures (above around half the critical temperature) we find that the predominant effect of the interface is to reduce the local density, which significantly suppresses the local cluster populations. At lower temperatures, however, pronounced interfacial layering is found. This appears to be connected with significant orientational ordering of clusters based on 3- and 5-membered rings, with the rings aligning perpendicular and parallel to the interface respectively. At all temperatures, we find that the population of five-fold symmetric structures is suppressed, rather than enhanced, close to the interface.Comment: 10 pages, 8 figures, accepted for publication by Molecular Physic