Related papers: COSMIC: A Galaxy Cluster Finding Algorithm Using M…
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…
Galaxy-scale strong gravitational lensing is not only a valuable probe of the dark matter distribution of massive galaxies, but can also provide valuable cosmological constraints, either by studying the population of strong lenses or by…
Sophisticated analysis of modern large-scale structure surveys requires mock catalogs. Mock catalogs are used to optimize survey design, test reduction and analysis pipelines, make theoretical predictions for basic observables and propagate…
The Cluster Lensing And Supernova survey with Hubble (CLASH) is a 524-orbit multi-cycle treasury program to use the gravitational lensing properties of 25 galaxy clusters to accurately constrain their mass distributions. The survey,…
Galaxies and galaxy clusters trace the same cosmic density field, but their statistics have been modeled separately in cosmological analyses. We present a unified, simulation-based framework to model them using the galaxy-halo connection.…
Galaxy clusters are the largest structures in Universe. They are very important as both cosmological probes and astrophysical laboratories. Several methods have been developed to detect galaxy clusters with different techniques (optical,…
Rich clusters of galaxies, the largest virialized systems known, provide a powerful tool for the study of cosmology. Some of the fundamental questions that can be addressed with clusters of galaxies include: how did galaxies and large-scale…
We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with…
We explore the capability of deep learning to classify cosmic structures. In cosmological simulations, cosmic volumes are segmented into voids, sheets, filaments and knots, according to the distribution and kinematics of dark matter (DM),…
Cosmological probes based on galaxy clusters rely on cluster number counts and large-scale structure information. X-ray cluster surveys are well suited for this purpose, since they are far less affected than optical surveys by projection…
We present Classification of Cluster GAlaxy MEmbers (C$^2$-GaMe), a classification algorithm based on a suite of machine learning models that differentiates galaxies into orbiting, infalling, and background (interloper) populations, using…
We present the first catalog of 67 strong galaxy-galaxy lens candidates discovered in the 1.64 square degree Hubble Space Telescope COSMOS survey. Twenty of these systems display multiple images or strongly curved large arcs. Our initial…
We developed an algorithm to find and characterize gravitationally lensed galaxies (arcs) to perform a comparison of the observed and simulated arc abundance. Observations are from the Cluster Lensing And Supernova survey with Hubble…
We present the ROGER (Reconstructing Orbits of Galaxies in Extreme Regions) code, which uses three different machine learning techniques to classify galaxies in, and around, clusters, according to their projected phase-space position. We…
Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the…
The classification of galaxy morphologies is an important step in the investigation of theories of hierarchical structure formation. While human expert visual classification remains quite effective and accurate, it cannot keep up with the…
Studies of galaxy clusters have proved crucial in helping to establish the standard model of cosmology, with a universe dominated by dark matter and dark energy. A theoretical basis that describes clusters as massive, multi-component,…
Taking advantage of $\sim4700$ deg$^2$ optical coverage of the Southern sky offered by the VST ATLAS survey, we construct a new catalogue of photometrically selected galaxy groups and clusters using the {\sc orca} cluster detection…
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…
We present a brief review of the history of optical searches of galaxy clusters, starting from that of Abell. The traditional application of this survey method suffers from contamination due to projection of galaxies along the line of…