Related papers: Topological Classification of Clusters in Condense…
Clustering plays an important role in the structure of nuclei, especially for light nuclei in the $p$-shell. In nuclear cluster models these degrees of freedom are introduced explicitly. In the Resonating Group Method or in the Generator…
In this paper, numerical simulations of four-mode continuous-variable cluster states with different topologies in the framework of measurement-based quantum computation are presented. By utilizing the symplectic representation and…
In L\"ovdal et al, we presented a data-driven method for clustering in Integrals of Motion space and applied it to a large sample of nearby halo stars with 6D phase-space information. We identified a large number of clusters, many of which…
In the present work we focus on future experiments using cluster abundance observations to constraint the Dark Energy equation of state parameter, w. To obtain tight constraints from this kind of experiment, a reliable sample of galaxy…
A novel nonparametric clustering algorithm is proposed using the interpoint distances between the members of the data to reveal the inherent clustering structure existing in the given set of data, where we apply the classical nonparametric…
A few-body cluster is a building block of a many-body system in a gas phase provided the temperature at most is of the order of the binding energy of this cluster. Here we illustrate this statement by considering a system of tubes filled…
In order to obtain morphological information of unlabeled galaxies, we present an unsupervised machine-learning (UML) method for morphological classification of galaxies, which can be summarized as two aspects: (1) the methodology of…
{The determination of cluster masses is a complex problem that would be aided by information about the cluster shape and orientation (along the line-of-sight).} {It is in this context, that we have developed a scheme for identifying the…
Star cluster formation in giant molecular clouds involves the local collapse of the cloud into small gas-rich subclusters, which can then subsequently collide and merge to build up the final star cluster(s). In this paper, we simulate…
Clustering algorithms are often used to find subpopulations in exploratory data analysis workflows. Not only the clusters themselves, but also their shape can represent meaningful subpopulations. In this paper, we present FLASC, an…
A cluster consisting of many atoms or molecules may be considered, in some circustances, to be a single large molecule with a well defined polarizability. Once the polarizability of such a cluster is known, one can evaluate certain…
It was shown that light nuclei such as $^4$He, $^8$Be, and $^{12}$C can be well described by RMF models, which enables a unified description for nuclei with baryon numbers $A\gtrsim4$. In this work, we propose a hybrid treatment for…
A cluster finding method was developed and applied in four Local Group Galaxies (SMC, M31, M33 and NGC 6822). The aim is to study the young stellar population of these galaxies by identifying stellar structures in small and large scales.…
We employ the constrained density functional theory to investigate cluster phenomena for the $^{12}$C nucleus. The proton and neutron densities are generated from the placement of three $^{4}$He nuclei (alpha particles) geometrically. These…
Since the pioneering discovery of molecular resonances in the 12C+12C reaction more than half a century ago a great deal of research work has been undertaken in alpha clustering. Our knowledge on physics of nuclear molecules has increased…
We use a cluster ensemble to determine the number of clusters, k, in a group of data. A consensus similarity matrix is formed from the ensemble using multiple algorithms and several values for k. A random walk is induced on the graph…
Spectral clustering has gained importance in recent years due to its ability to cluster complex data as it requires only pairwise similarity among data points with its ease of implementation. The central point in spectral clustering is the…
We study the large sample behavior of a convex clustering framework, which minimizes the sample within cluster sum of squares under an~$\ell_1$ fusion constraint on the cluster centroids. This recently proposed approach has been gaining in…
We investigate the efficacy of using the cosmic web nodes identified by the DisPerSE topological filament finder to systematically identify galaxy groups in the infall regions around massive clusters. The large random motions and infall…
A general dynamical cluster identification framework including both modeling and computation is developed. The earthquake declustering problem is studied to demonstrate how this framework applies. A stochastic model is proposed for…