Related papers: Galaxy stellar and total mass estimation using mac…
This chapter reviews the application of Artificial Intelligence (AI) techniques to the study of galaxy clusters, covering both theoretical developments and their use as tools to infer cluster properties from a variety of observational…
The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the…
This work observationally addresses the relative distribution of total and optically luminous matter in galaxy clusters by computing the radial profile of the stellar-to-total mass ratio. We adopt state-of-the-art accurate lensing masses…
Typically, stars in galaxies have higher velocities than predicted by Newtonian gravity in conjunction with observable galactic matter. To account for the phenomenon, some researchers modified Newtonian gravitation; others introduced dark…
Tens of thousands of galaxy-galaxy strong lensing systems are expected to be discovered by the end of the decade. These will form a vast new dataset that can be used to probe subgalactic dark matter structures through its gravitational…
Using a combined analysis of strong lensing and galaxy dynamics, we characterize the mass distributions and M/L ratios of galaxy groups, which form an important transition regime in Lambda-CDM cosmology. By mapping the underlying mass…
Cosmological surveys aim at answering fundamental questions about our Universe, including the nature of dark matter or the reason of unexpected accelerated expansion of the Universe. In order to answer these questions, two important…
Dark matter comprises the bulk of the matter in the universe but its particle nature and cosmological origin remain mysterious. Knowledge of the dark matter density distribution in the Milky Way Galaxy is crucial to both our understanding…
Properties of massive galaxy clusters, such as mass abundance and concentration, are sensitive to cosmology, making cluster statistics a powerful tool for cosmological studies. However, favoring a more simplified, spherically symmetric…
Large galaxies may contain an "atmosphere" of hot interstellar X-ray gas, and the temperature and radial density profile of this gas can be used to measure the total mass of the galaxy contained within a given radius r. We use this…
Galaxy clusters are a promising probe of late-time structure growth, but constraints on cosmology from cluster abundances are currently limited by systematics in their inferred masses. One unmitigated systematic effect in weak-lensing mass…
Using a sample of 126000 late-type galaxies from the SDSS, we analyzed stellar mass as a function of the dynamical mass. Stellar masses are estimated using eight stellar population synthesis (SPS) models with constant IMF, while dynamical…
Current data broadly support trends of galaxy surface brightness profile amplitude and shape with total stellar mass predicted by state-of-the-art Lambda-CDM cosmological simulations, although recent results show signs of interesting…
Starburst galaxies are often found to be the result of galaxy mergers. As a result, galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlation between stellar mass and star formation rate. Here, we aim to…
We report the results of a multi-waveband analysis of the masses and luminosities of $\sim$600 galaxy groups and clusters identified in the maxBCG catalogue. These data are intended to form the basis of future work on the formation of the…
Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations. However, to compare data with these simulations, the simulations in…
We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference. We employ Graph Neural Networks (GNNs), architectures designed to…
The science behind galaxy interaction and mergers has a fundamental role and gives us an insight into galaxy formation and its evolution. Fluctuating angular momentum is responsible for extraordinary events like polar rings, tidal tails,…
We investigate a series of galaxy properties computed using the merger trees and environmental histories from dark matter only cosmological simulations, using a semi-recurrent neural network producing self-consistent predictions of galaxy…
A galaxy's morphological features encode details about its gas content, star formation history, and feedback processes, which play important roles in regulating its growth and evolution. We use deep convolutional neural networks (CNNs) to…