Related papers: Groups and clusters detection on DPOSS catalogs
The paper investigates the problem of finding communities in complex network systems, the detection of which allows a better understanding of the laws of their functioning. To solve this problem, two approaches are proposed based on the use…
We present the clustering of galaxy clusters as a useful addition to the common set of cosmological observables. The clustering of clusters probes the large-scale structure of the Universe, extending galaxy clustering analysis to the…
Clusters of galaxies are the most massive objects in the Universe and mapping their location is an important astronomical problem. This paper describes an algorithm (based on statistical signal processing methods), a software architecture…
I present selected results from a wide-field CCD survey of the globular cluster systems of giant galaxies, including showing how measurements of the specific frequency of metal-poor globular clusters can constrain the redshift of their…
We study the evolution of galaxies in clusters by the analysis of a sample of about 3000 galaxies, members of 59 clusters from the ESO Nearby Abell Cluster Survey (ENACS). We distinguish four cluster galaxy populations, based on their…
We describe the construction of catalogues of galaxy clusters from the APM Galaxy survey using an automated algorithm based on Abell-like selection criteria. We investigate the effects of varying several parameters in our selection…
We investigate, using simulated galaxy catalogues, the completeness of searches for massive clusters of galaxies in redshift surveys or imaging surveys with photometric redshift estimates, i.e. what fraction of clusters (M>10^14/h Msun) are…
We present a catalogue of galaxy clusters detected in the Planck all-sky Compton parameter maps and identified using data from the WISE and SDSS surveys. The catalogue comprises about 3000 clusters in the SDSS fields. We expect the…
We present the first dynamical study of the optically selected Palomar Distant Cluster Survey (PDCS). We show that eleven out of twelve PDCS clusters we observed are real over-densities of galaxies. Most clusters have velocity dispersions…
We propose the DPSM method, a density-based node clustering approach that automatically determines the number of clusters and can be applied in both data space and graph space. Unlike traditional density-based clustering methods, which…
Correlations among the independently measured physical properties of globular clusters (GCs) can provide powerful tests for theoretical models and new insights into their dynamics, formation, and evolution. We review briefly some of the…
The results of a search for distant clusters of galaxies performed using the I-band data obtained by the ESO Imaging Survey (EIS) are presented. Cluster candidates are identified using a matched filter algorithm, that provides not only an…
We develop a method to identify proto-clusters based on dark matter halos represented by galaxy groups selected from surveys of galaxies at high redshift. We test the performance of this method on halos in N-body simulations, and find it…
Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…
We review empirical and theoretical findings concerning white dwarfs in Galactic globular clusters. Since their detection is a critical issue we describe in detail the various efforts to find white dwarfs in globular clusters. We then…
Identifying possible clusters in datasets and estimating their overall modularity are central tasks in pattern recognition. In the present work, concepts and methodologies are described for performing these tasks while considering only the…
Given the importance of clusters to the fields of cosmology and galaxy evolution, it is critical to understand how the cluster detection process affects (biases) ones scientific conclusions derived from a given cluster sample. I review the…
A novel approach rooted on the notion of consensus clustering, a strategy developed for community detection in complex networks, is proposed to cope with the heterogeneity that characterizes connectivity matrices in health and disease. The…
Community detection, which focuses on clustering nodes or detecting communities in (mostly) a single network, is a problem of considerable practical interest and has received a great deal of attention in the research community. While being…
Software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software…