Related papers: The detection of globular clusters in galaxies as …

Galaxy clusters are useful laboratories to investigate the evolution of the Universe, and accurately measuring their total masses allows us to constrain important cosmological parameters. However, estimating mass from observations that use…

Clusters of galaxies as gravitational lenses allow to study the stellar content and properties of high-z galaxies much fainter than the usual spectroscopic field surveys. We review the recent results obtained on the identification and study…

A Deep Learning (DL) algorithm is built and tested for its ability to determine centers of star images on HST/WFPC2 exposures, in filters F555W and F814W. These archival observations hold great potential for proper-motion studies, but the…

We argue that globular clusters (GCs) are good candidates for gravitational lenses in explaining quasar-galaxy associations. The catalog of associations (Bukhmastova 2001) compiled from the LEDA catalog of galaxies (Paturel 1997) and from…

Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify…

In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are dissimilar. This problem was rarely studied previously,…

We present an automated statistical method that uses medium-resolution spectroscopic observations of a set of stars to select those that show evidence of possessing significant amounts of neutron-capture elements. Our tool was tested…

The classification of galaxy morphology is a hot issue in astronomical research. Although significant progress has been made in the last decade in classifying galaxy morphology using deep learning technology, there are still some…

Large photometric surveys provide a rich source of observations of quiescent galaxies, including a surprisingly large population at z>1. However, identifying large, but clean, samples of quiescent galaxies has proven difficult because of…

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…

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…

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 present the results of methodological works on automated analysis of the large scale distribution of galaxies. Selecting candidates for clusters and groups of galaxies was carried out using two complementary methods of determining the…

Traditional clustering algorithms often focus on the most fine-grained information and achieve clustering by calculating the distance between each pair of data points or implementing other calculations based on points. This way is not…

Density-based clustering methodology has been widely considered in the statistical literature for classifying Euclidean observations. However, this approach has not been contemplated for directional data yet. In this work, directional…

Broad searches for continuous gravitational wave signals rely on hierarchies of follow-up stages for candidates above a given significance threshold. An important step to simplify these follow-ups and reduce the computational cost is to…

Deep optical images are often crowded with overlapping objects. This is especially true in the cores of galaxy clusters, where images of dozens of galaxies may lie atop one another. Accurate measurements of cluster properties require…

We present a newly developed code that allows simulations of optical observations of galaxy fields with a variety of instruments. The code incorporates gravitational lensing effects and is targetted at simulating lensing by galaxy clusters.…

The Universe expansion rate is modulated around local inhomogeneities due to their gravitational potential. Velocity waves are then observed around galaxy clusters in the Hubble diagram. This paper studies them in a ~738 Mpc wide, with…

Machine learning methods are increasingly helping astronomers identify new radio pulsars. However, they require a large amount of labelled data, which is time consuming to produce and biased. Here we describe a Semi-Supervised Generative…