Related papers: SDSS DR7 superclusters. Principal component analys…

Recently, the observed cellular nature of the large-scale structure of the Universe with its quasi-regular pattern of superclusters and voids has been pointed out by several authors. In this paper, we investigate properties of the initial…

Deep clustering incorporates embedding into clustering to find a lower-dimensional space appropriate for clustering. In this paper, we propose a novel deep clustering framework with self-supervision using pairwise similarities (DCSS). The…

We analyse the first publicly released deep field of the UKIDSS Deep eXtragalactic Survey (DXS) to identify candidate galaxy over-densities at z~1 across ~1 sq. degree in the ELAIS-N1 field. Using I-K, J-K and K-3.6um colours we identify…

Using a subsample of 79 nearby clusters from the RASS-SDSS galaxy cluster catalogue of Popesso et al. (2005a), we perform a regression analysis between the cluster integrated star formation rate (Sigma_SFR) the cluster total stellar mass…

We study the dependence of the properties of group galaxies on the surrounding large-scale environment, using SDSS-DR7 data. Galaxies are ranked according to their luminosity within each group and classified morphologically by the S\'ersic…

The relative orientation of clusters' major elongation axes and clusters' angular momentum is studied using a large N-body simulation in a box of 500 Mpc/h base length for a standard LambdaCDM model. Employing the technique of mark…

In high-dimension, low-sample size (HDLSS) data, it is not always true that closeness of two objects reflects a hidden cluster structure. We point out the important fact that it is not the closeness, but the "values" of distance that…

We propose a new approach for clustering DNA features using array CGH data from multiple tumor samples. We distinguish data-collapsing: joining contiguous DNA clones or probes with extremely similar data into regions, from clustering:…

Matrices are two-dimensional data structures allowing one to conceptually organize information. For example, adjacency matrices are useful to store the links of a network; correlation matrices are simple ways to arrange gene co-expression…

We present a computational study of assembling carbon clusters and nanophases in space from carbon aggregations. Geometry optimizations and Density-functional-based tight-binding (SCC-DFTB) dynamics methods are employed to predict carbon…

Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the Universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the…

We investigate whether the dynamical status of clusters is related to the large-scale structure of the Universe. We find that cluster substructure is strongly correlated with the tendency of clusters to be aligned with their nearest…

The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…

We use high resolution dissipationless N-body simulations to examine the spatial distribution of galaxy clusters on large scales. The Standard CDM model and two of its main competitors, Low density CDM and Mixed Dark Matter are compared.The…

Galaxy clusters provide a unique environment to study galaxy evolution. The role of cluster dynamical states in shaping the physical and morphological properties of member galaxies remains an open question. We aim to assess the impact of…

We present the results of our dynamical state proxy measurements performed on 28 strong lensing galaxy clusters from the Sloan Giant Arcs Survey (SGAS). Using Chandra ACIS-I/S X-ray data supplemented with HST WFC3 imaging, we measure four…

Although coherent large-scale structures such as filaments and walls are apparent to the eye in galaxy redshift surveys, they have so far proven difficult to characterize with computer algorithms. This paper presents a procedure that uses…

We study large-scale structures from numerical simulations, paying particular attention to supercluster-like structures. A grid-density-contour based algorithm is adopted to locate connected groups. With the increase of the linking density…

A deep clustering model conceptually consists of a feature extractor that maps data points to a latent space, and a clustering head that groups data points into clusters in the latent space. Although the two components used to be trained…

We study the relative fraction of galaxy morphological types in clusters, as a function of the projected local galaxy density and different global parameters: cluster projected gas density, cluster projected total mass density , and reduced…