Related papers: A group finder algorithm optimised for the study o…
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…
Traditional clustering algorithms often struggle with high-dimensional and non-uniformly distributed data, where low-density boundary samples are easily disturbed by neighboring clusters, leading to unstable and distorted clustering…
We apply a friends-of-friends algorithm to an enhanced SDSS DR12 spectroscopic catalog including redshift from literature to construct a catalog of $1588~N\ge3$ compact groups of galaxies containing 5179 member galaxies and covering the…
Many catalogues of isolated compact groups of galaxies (CGs) have been extracted using Hickson's criteria to identify isolated, dense systems of galaxies, with at least three or four galaxies concordant in magnitude and redshift. But is not…
The environment plays a critical role in galaxy evolution, with galaxy clusters and their infall regions offering diverse conditions that shape galaxies before they enter the dense cluster core, a process known as ``pre-processing''.…
Density-based clustering is the task of discovering high-density regions of entities (clusters) that are separated from each other by contiguous regions of low-density. DBSCAN is, arguably, the most popular density-based clustering…
We describe and apply a simple prescription for defining connected structures in galaxy redshift surveys. The method is based upon two passes with a friends-of-friends groupfinder. The first pass uses a cylindrical linking volume to find…
Classification of galaxies is traditionally associated with their morphologies through visual inspection of images. The amount of data to come renders this task inhuman and Machine Learning (mainly Deep Learning) has been called to the…
How can we find a good graph clustering of a real-world network, that allows insight into its underlying structure and also potential functions? In this paper, we introduce a new graph clustering algorithm Dcut from a density point of view.…
Data clustering with uneven distribution in high level noise is challenging. Currently, HDBSCAN is considered as the SOTA algorithm for this problem. In this paper, we propose a novel clustering algorithm based on what we call graph of…
The detection of galaxy clusters in present and future surveys enables measuring mass-to-light ratios, clustering properties, galaxy cluster abundances and therefore, constraining cosmological parameters. We present a new technique for…
We train three convolutional neural networks (CNNs) to classify galaxies with Galaxy Zoo 2 dataset and extract the activations from the last fully connected layer or the last average pooling layer of CNNs to study the high-dimensional…
We present a new method for the mitigation of observational systematic effects in angular galaxy clustering via corrective random galaxy catalogues. Real and synthetic galaxy data, from the Kilo Degree Survey's (KiDS) 4$^{\rm{th}}$ Data…
We present a new algorithm to detect inter-cluster galaxy filaments based upon the assumption that the orientations of constituent galaxies along such filaments are non-isotropic. We apply the algorithm to the 2dF Galaxy Redshift Survey…
We identify compact groups of galaxies (CGs) in the IllustrisTNG-300 simulation using a Friends-of-Friends (FoF) algorithm. Our approach is designed to be comparable to systematic CG searches based on spectroscopic surveys, while avoiding…
Clusters of galaxies are important laboratories for understanding both galaxy evolution and constraining cosmological quantities. Any analysis of clusters, however, is best done when one can reliably determine which galaxies are members of…
We present EasyCritics, an algorithm to detect strongly-lensing groups and clusters in wide-field surveys without relying on a direct recognition of arcs. EasyCritics assumes that light traces mass in order to predict the most likely…
Compact groups (CGs) of galaxies -- relatively poor groups of galaxies in which the typical separations between members is of the order of a galaxy diameter -- offer an exceptional laboratory for the study of dense galaxian environments…
Strong Lensing is a powerful probe of the matter distribution in galaxies and clusters and a relevant tool for cosmography. Analyses of strong gravitational lenses with Deep Learning have become a popular approach due to these astronomical…
The purpose of this work is to perform a statistical analysis of the location of compact groups in the Universe from observational and semi-analytical points of view. We used the velocity-filtered compact group sample extracted from the Two…