Related papers: ROGER: Reconstructing Orbits of Galaxies in Extrem…

We use a matched filter algorithm to find and study clusters in both N-body simulations artificially populated with galaxies and the 2MASS survey. In addition to numerous checks of the matched filter algorithm, we present results on the…

The determination of galaxy merger fraction of field galaxies using automatic morphological indices and photometric redshifts is affected by several biases if observational errors are not properly treated. Here, we correct these biases…

We have reanalyzed a data set of 99 low redshift ($ z < 0.1 $) Abell clusters and determined their shapes. For this, three different measures are used. We use Monte-Carlo simulations to investigate the errors in the methods. The corrected…

We present the results of a weak lensing survey of six high-redshift (z > 0.5), X-ray selected clusters of galaxies. We have obtained ultra-deep R-band images of each cluster with the Keck Telescope, and have measured a weak lensing signal…

We present a machine-learning framework, Machine Inferred Galaxy (MIG), to populate dark-matter haloes with galaxies in N-body simulations. MIG predicts stellar mass ($M_\ast$), star-formation rate (SFR), atomic and molecular gas masses…

We present a deep machine learning (ML) approach to constraining cosmological parameters with multi-wavelength observations of galaxy clusters. The ML approach has two components: an encoder that builds a compressed representation of each…

We study the assembly of globular clusters (GCs) in 9 galaxy clusters using the cosmological simulation Illustris. GCs are tagged to individual galaxies at their infall time. The tidal removal of GCs from their galaxies and the distribution…

We present a machine learning search for local, low-mass galaxies ($z < 0.02$ and $10^6 M_\odot < M_* < 10^9 M_\odot$) using the combined photometric data from the DESI Imaging Legacy Surveys and the WISE survey. We introduce the spectrally…

We discover alignment of galaxies in clusters by analyzing the distribution of their position angles. We assume that galaxies are aligned, if their number at one 90deg position angle interval is more than twice higher than at another 90deg…

Starting from the observed distribution of galaxy clusters in redshift space we use a two--step procedure to recover their distances and peculiar velocities. After statistically correcting for the unobserved cluster distribution in the zone…

Machine learning is a powerful technique, becoming increasingly popular in astrophysics. In this paper, we apply machine learning to more than a thousand globular cluster (GC) models simulated as part of the 'MOCCA-Survey Database I'…

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 identify gravitationally bound structures in the Ursa Major region using positions, velocities and photometry from the Sloan Digital Sky Survey (SDSS DR7) and the Third Reference Catalogue of Bright Galaxies (RC3). A friends-of-friends…

Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This…

In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…

We present an analysis of the spatial distribution of globular cluster (GC) systems of 118 nearby early-type galaxies in the Next Generation Virgo Cluster Survey (NGVS) and Mass Assembly of early-Type GaLAxies with their fine Structures…

This research investigates how Machine Learning (ML) algorithms can assist in workload allocation strategies by detecting tasks with node affinity operators (referred to as constraint operators), which constrain their execution to a limited…

[abridged] New near-infrared surveys, using the HST, offer an unprecedented opportunity to study rest-frame optical galaxy morphologies at z>1 and to calibrate automated morphological parameters that will play a key role in classifying…

Context. The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution. Aims. Here, we report the use of machine learning techniques to…

We develop a method for reconstructing the two-dimensional, projected gravitational potential of galaxy clusters from observed line-of-sight velocity dispersions of cluster galaxies. It is the third of an intended series of papers aiming at…