Related papers: An Analytic Model of Superclusters
We present the results of a redshift survey of both cluster and intercluster galaxies in the central part of the Shapley Concentration, the richest supercluster of clusters in the nearby Universe, consisting of ~2000 radial velocities. We…
(abridged) We present the results of a redshift survey of intercluster galaxies in the central region of the Shapley Concentration supercluster, aimed at determining the distribution of galaxies in between obvious overdensities. Our sample…
We investigate the dynamical state of superclusters in Lambda cold dark matter ($\Lambda$CDM) cosmological models, where the density parameter $\Omega_0=0.2-0.4$ and $\sigma_8$ (the rms fluctuation on the $8h^{-1}$Mpc scale) is $0.7-0.9$.…
Using the superstatistics method, we propose an extension of the random matrix theory to cover systems with mixed regular-chaotic dynamics. Unlike most of the other works in this direction, the ensembles of the proposed approach are basis…
We investigate properties of the correlation function of clusters of galaxies using geometrical models. On small scales the correlation function depends on the shape and the size of superclusters. On large scales it describes the geometry…
Clustering aims to divide a set of points into groups. The current paradigm assumes that the grouping is well-defined (unique) given the probability model from which the data is drawn. Yet, recent experiments have uncovered several…
We calculate the multiplicity function of matter condensations by directly considering the actual, deeply non-linear density field, which we compare to the popular Press-Schechter approximation. We show the mass function is a function of a…
Studies of disordered heterogeneous media and galaxy cosmology share a common goal: analyzing the distribution of particles at `microscales' to predict physical properties at `macroscales', whether for a liquid, composite material, or…
Protoclusters, which will yield galaxy clusters at lower redshift, can provide valuable information on the formation of galaxy clusters. However, identifying progenitors of galaxy clusters in observations is not an easy task, especially at…
Galaxy environment is frequently discussed, but inconsistently defined. It is especially difficult to measure at high redshift where only photometric redshifts are available. With a focus on early forming proto-clusters, we use a…
Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces. For these models, posterior inference methods can be…
The large supercluster structures obvious in recent galaxy redshift surveys are quantified using an one-dimensional cluster analysis (core sampling) and a three-dimensional cluster analysis based on the minimal spanning tree. The comparison…
Clustering can be defined as the process of assembling objects into a number of groups whose elements are similar to each other in some manner. As a technique that is used in many domains, such as face clustering, plant categorization,…
In two previous papers a semi-analytical model was presented for the hierarchical clustering of halos via gravitational instability from peaks in a random Gaussian field of density fluctuations. This model is better founded than the…
Cluster analysis of very high dimensional data can benefit from the properties of such high dimensionality. Informally expressed, in this work, our focus is on the analogous situation when the dimensionality is moderate to small, relative…
We use large volume, high resolution, N-body simulations of 3 different $\Lambda$CDM models, with different clustering strengths, to generate dark matter halo merging histories. Over the reliable range of halo masses, roughly galaxy groups…
Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning…
Clustering is an unsupervised learning problem that aims to partition unlabelled data points into groups with similar features. Traditional clustering algorithms provide limited insight into the groups they find as their main focus is…
We study the stellar cluster population in two adjacent fields in the nearby, face-on spiral galaxy, M83, using WFC3/HST imaging. The clusters are selected through visual inspection to be centrally concentrated, symmetric, and resolved on…
The largest and the deepest super-structure known today is the Shapley super-cluster. This is the sky area with the highest over-density of galaxy clusters and therefore also an ideal region to test the effects of a high density environment…