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Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological…

Cosmology and Nongalactic Astrophysics · Physics 2023-08-09 G. Angora , P. Rosati , M. Meneghetti , M. Brescia , A. Mercurio , C. Grillo , P. Bergamini , A. Acebron , G. Caminha , M. Nonino , L. Tortorelli , L. Bazzanini , E. Vanzella

Galaxy clusters can be detected as surface brightness enhancements in smoothed optical surveys. This method does not require individual galaxies to be identifiable, and enables clusters to be detected out to surprisingly high redshifts, as…

Astrophysics · Physics 2009-11-07 Matthias Bartelmann , Simon D. M. White

The majority of galaxy group catalogues available in the literature use the popular friends-of-friends algorithm which links galaxies using a linking length. One potential drawback to this approach is that clusters of point can be link with…

Astrophysics of Galaxies · Physics 2023-07-19 Mark T. Graham , Michele Cappellari

Galaxy clusters are the most massive gravitationally bound structures in the Universe and key probes of cosmic evolution. The large data volume expected from upcoming surveys requires efficient automated analysis methods for tens of…

Cosmology and Nongalactic Astrophysics · Physics 2026-05-06 M. Fogliardi , M. Meneghetti , C. Giocoli , L. Moscardini , P. Rosati , L. Leuzzi , G. Angora , L. Bazzanini , C. Spinelli

Based on a recent photometric redshift galaxy catalogue, we have searched for galaxy clusters in the Stripe~82 region of the Sloan Digital Sky Survey by applying the Adami & MAzure Cluster FInder (AMACFI). Extensive tests were made to…

We introduce Deep-CEE (Deep Learning for Galaxy Cluster Extraction and Evaluation), a proof of concept for a novel deep learning technique, applied directly to wide-field colour imaging to search for galaxy clusters, without the need for…

Astrophysics of Galaxies · Physics 2019-11-26 Matthew C. Chan , John P. Stott

The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the…

Galaxy cluster-scale strong gravitational lensing systems are rare yet valuable tools for investigating the properties of dark matter and dark energy, as well as providing the opportunity to study the distant universe at flux levels and…

Astrophysics of Galaxies · Physics 2026-01-19 Zhejian Zhang , Nan Li , Shude Mao , Hu Zou , Zizhao He , Mingxiang Fu , Shenzhe Cui

The survey data of Wide-field Infrared Survey Explorer (WISE) provide an opportunity for the identification of galaxy clusters. We present an efficient method for detecting galaxy clusters by combining the WISE data with SuperCOSMOS and…

Astrophysics of Galaxies · Physics 2014-07-21 W. W. Xu , Z. L. Wen , J. L. Han

Context: The VISTA Variables in the Via Lactea (VVV) and its extension (VVVX) are near-infrared surveys mapping the Galactic bulge and adjacent disk. These data have enabled the discovery of numerous star clusters obscured by high and…

We describe the scientific motivation behind, and the methodology of, the Stanford Cluster Search (StaCS), a program to compile a catalog of optically selected clusters of galaxies at intermediate and high (0.3 < z < 1) redshifts. The…

Imaging data from the Sloan Digital Sky Survey is used to measure the empirical size-richness relation for a large sample of galaxy clusters. Using population subtraction methods, we determine the radius at which the cluster galaxy number…

We use the 2dF Galaxy Redshift Survey data to compile catalogues of superclusters for the Northern and Southern regions of the 2dFGRS, altogether 543 superclusters at redshifts 0.009 < z < 0.2. We analyse methods of compiling supercluster…

We have used public data from the Next Generation Virgo Survey (NGVS) to investigate the dwarf galaxy population of the Virgo cluster beyond what has previously been discovered. We initially mask and smooth the data, and then use the object…

Astrophysics of Galaxies · Physics 2016-01-27 J. I. Davies , L. J. M. Davies , O. C. Keenan

We review recent advancements in cosmology with galaxy clusters. Galaxy clusters are the most massive objects in the Universe. Consequently the cluster number density as a function of cluster mass, or cluster abundance, is sensitive to…

Cosmology and Nongalactic Astrophysics · Physics 2025-05-13 Hironao Miyatake

In order to study the status and the possible evolution of clusters of galaxies at intermediate redshifts (z ~ 0.1 - 0.3), as well as their spatial correlation and relationship with the local environment, we built a sample of candidate…

Astrophysics · Physics 2009-11-07 A. Zanichelli , M. Vigotti , R. Scaramella , G. Grueff , G. Vettolani

A methodology is developed to identify clusters in a bulk phase which are topologically equivalent to certain reference clusters. The selected reference clusters are the Morse clusters with 13 or less atoms and the 13 atom clusters found in…

Soft Condensed Matter · Physics 2007-05-23 Stephen R. Williams

We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive…

Cosmology and Nongalactic Astrophysics · Physics 2020-02-26 P. A. A. Lopes , A. L. B. Ribeiro

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…

Currently, density-based clustering algorithms are widely applied because they can detect clusters with arbitrary shapes. However, they perform poorly in measuring global density, determining reasonable cluster centers or structures,…

Machine Learning · Computer Science 2023-11-02 Mingjie Cai , Zhishan Wu , Qingguo Li , Feng Xu , Jie Zhou
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