Related papers: CALSAGOS: Clustering ALgorithmS Applied to Galaxie…
Conservative mass limits are often imposed on the dark matter halo catalogues extracted from N-body simulations. By comparing simulations with different mass resolutions, at $z=0$ we find that even for halos resolved by 100 particles, the…
Hierarchical clustering is a fundamental task often used to discover meaningful structures in data, such as phylogenetic trees, taxonomies of concepts, subtypes of cancer, and cascades of particle decays in particle physics. Typically…
We present 279 galaxy cluster candidates at $z > 1.3$ selected from the 94 deg$^{2}$ Spitzer South Pole Telescope Deep Field (SSDF) survey. We use a simple algorithm to select candidate high-redshift clusters of galaxies based on…
The identification of substructures within halos in cosmological hydrodynamical simulations is a fundamental step to identify the simulated counterparts of real objects, namely galaxies. For this reason, substructure finders play a crucial…
Super-resolution imaging techniques have largely improved our capabilities to visualize nanometric structures in biological systems. Their application further enables one to potentially quantitate relevant parameters to determine the…
We introduce a new kinematic method to investigate the structure of open star clusters. We adopt a hierarchical clustering algorithm that uses the celestial coordinates and the proper motions of the stars in the field of view of the cluster…
We present a study of the properties of the star-cluster systems around pseudo-bulges of late-type spiral galaxies using a sample of 11 galaxies with distances from 17 to 37 Mpc. Star clusters are identified from multiband HST ACS and WFPC2…
We present a novel approach for finding and evaluating structural models of small metallic nanoparticles. Rather than fitting a single model with many degrees of freedom, the approach algorithmically builds libraries of nanoparticle…
We present an optimised galaxy cluster finder, 3D-Matched-Filter (3D-MF), which utilises galaxy cluster radial profiles, luminosity functions and redshift information to detect galaxy clusters in optical surveys. This method is an…
We present preliminary results of a simple peak finding algorithm applied to catalogues of galaxies, extracted from the Second Palomar Sky Survey in the framework of the CRoNaRio project. All previously known Abell and Zwicky clusters in a…
Classification with a sparsity constraint on the solution plays a central role in many high dimensional machine learning applications. In some cases, the features can be grouped together so that entire subsets of features can be selected or…
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…
A new method for a simultaneous search for clusters of galaxies in X-ray photon maps and optical galaxy maps is described. The merging of X-ray and optical data improves the source identification so that a large amount of telescope time for…
Wide-field cosmological surveys provide hundreds of thousands of spectroscopically confirmed galaxy groups and clusters, valuable for tracing baryonic matter distribution. However, controlling systematics in identifying host dark matter…
In order to help facilitate the future study of ultra-diffuse galaxies (UDGs) we compile a catalogue of their spectroscopic properties. Using it, we investigate some of the biases inherent in the current UDG sample that have been targeted…
Abridged] We present here new results on the space density of rich, optically-selected, clusters of galaxies at low redshift (z<0.15). These results are based on the application of the matched filter cluster-finding algorithm to 1067 deg2…
We present an optically selected galaxy cluster catalog from ~ 2,700 square degrees of the Digitized Second Palomar Observatory Sky Survey (DPOSS), spanning the redshift range 0.1 < z < 0.5, providing an intermediate redshift supplement to…
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…
We present a detailed study of the morphological features of 22 rich galaxy clusters. Our sample is constructed from a cross-correlation of optical (Abell+APM) data with X-ray (0.1 - 2.4) keV ROSAT pointed observations. We systematically…
When approaching a clustering problem, choosing the right clustering algorithm and parameters is essential, as each clustering algorithm is proficient at finding clusters of a particular nature. Due to the unsupervised nature of clustering…