Related papers: sOPTICS: A Modified Density-Based Algorithm for Id…

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…

We present the first results of a serendipitous search for clusters of galaxies in deep ROSAT-PSPC pointed observations at high galactic latitude. The survey is being carried out using a Wavelet based Detection Algorithm which is not biased…

We develop an improved mass tracer for clusters of galaxies from optically observed parameters, and calibrate the mass relation using weak gravitational lensing measurements. We employ a sample of ~ 13,000 optically-selected clusters from…

Cluster cosmology depends critically on how optical clusters are selected from imaging surveys. We compare the conditional luminosity function (CLF) and weak lensing halo masses between two different cluster samples at fixed richness,…

In this paper we explore the use of spatial clustering algorithms as a new computational approach for modeling the cosmic web. We demonstrate that such algorithms are efficient in terms of computing time needed. We explore three distinct…

We measure the ellipticity of isolated clusters of galaxies in the Sloan Digital Sky Survey (SDSS) using gravitational lensing. We stack the clusters, rotating so that the major axes of the ellipses determined by the positions of cluster…

Context. Clusters of galaxies are important for cosmology and astrophysics. They may be discovered through either the summed optical/IR radiation originating from their member galaxies or via X-ray emission originating from the hot…

Cluster analysis which focuses on the grouping and categorization of similar elements is widely used in various fields of research. Inspired by the phenomenon of atomic fission, a novel density-based clustering algorithm is proposed in this…

We investigate how observations of strong lensing can be used to infer cosmological parameters, in particular the equation of state of dark energy. We focus on the growth of the critical lines of lensing clusters with the source redshift as…

The clustering of matter on cosmological scales is an essential probe for studying the physical origin and composition of our Universe. To date, most of the direct studies have focused on shear-shear weak lensing correlations, but it is…

We have used a star-count algorithm based the Panoramic Survey Telescope And Rapid Response System 3$\pi$ survey data aim to identify and characterize uncharted open clusters (OCs). With limiting magnitudes of about 22 mag in \gps, \rps,…

Data from a new, wide field, coincident optical and X-ray survey, the X-ray Dark Cluster Survey (XDCS) are presented. This survey comprises simultaneous and independent searches for clusters of galaxies in the optical and X-ray passbands.…

We describe an automated method, the Cut & Enhance method (CE) for detecting clusters of galaxies in multi-color optical imaging surveys. This method uses simple color cuts, combined with a density enhancement algorithm, to up-weight pairs…

We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that…

We present a new algorithm, CAMIRA, to identify clusters of galaxies in wide-field imaging survey data. We base our algorithm on the stellar population synthesis model to predict colours of red-sequence galaxies at a given redshift for an…

We examine the relationship between the total X-ray and optical luminosities of groups and clusters of galaxies taken from various samples in the literature. The clusters and groups were drawn from four different catalogs: (1) the Abell/ACO…

We analyse a catalogue of simulated clusters within the theoretical framework of the Spherical Collapse Model (SCM), and demonstrate that the relation between the infall velocity of member galaxies and the cluster matter overdensity can be…

We present a new cluster catalog extracted from the Sloan Digital Sky Survey Data Release 6 (SDSS DR6) using an adaptive matched filter (AMF) cluster finder. We identify 69,173 galaxy clusters in the redshift range 0.045 $\le z <$ 0.78 in…

In this work we explore the possibility of applying machine learning methods designed for one-dimensional problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly…

We develop the Blooming Tree Algorithm, a new technique that uses spectroscopic redshift data alone to identify the substructures and the surrounding groups of galaxy clusters, along with their member galaxies. Based on the estimated…