Related papers: Dynamical mass inference of galaxy clusters with n…
We present a statistical comparison of three different estimates of cluster mass, namely, the dynamical masses obtained from the velocity dispersion of optical galaxies, the X-ray masses measured from the temperature of X-ray emitting gas…
We study the effect of contamination by interlopers in kinematic samples of galaxy clusters. We demonstrate that without the proper removal of interlopers the inferred parameters of the mass distribution in the cluster are strongly biased…
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…
One emerging application of machine learning methods is the inference of galaxy cluster masses. In this note, machine learning is used to directly combine five simulated multiwavelength measurements in order to find cluster masses. This is…
Existing machine learning methods for causal inference usually estimate quantities expressed via the mean of potential outcomes (e.g., average treatment effect). However, such quantities do not capture the full information about the…
We present a novel method to infer the Dark Matter (DM) content and spatial distribution within galaxies, based on convolutional neural networks trained within state-of-the-art hydrodynamical simulations (Illustris TNG100). The framework we…
We give the first determination of the observed peculiar velocity distribution function for a representative sample of galaxies (within 50 Mpc (H=100) of the Local Group), which includes a wide range of clustering properties. We explore in…
Galaxy cluster masses, rich with cosmological information, can be estimated from internal dark matter (DM) velocity dispersions, which in turn can be observationally inferred from satellite galaxy velocities. However, galaxies are biased…
The morphological classification of galaxies is considered a relevant issue and can be approached from different points of view. The increasing growth in the size and accuracy of astronomical data sets brings with it the need for the use of…
Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological…
We explore the detectability of bulk motions in the X-ray emitting intracluster medium (ICM) using a catalog of 1,836 mock Astro-E2 observations of simulated clusters of galaxies. We generate high resolution mock spectra for two observing…
Gas motions in the hot intracluster medium of galaxy clusters have an important effect on the mass determination of the clusters through X-ray observations. The corresponding dynamical pressure has to be accounted for in addition to the…
We introduce the state-of-the-art deep learning Denoising Diffusion Probabilistic Model (DDPM) as a method to infer the volume or number density of giant molecular clouds (GMCs) from projected mass surface density maps. We adopt…
In hierarchical clustering, galaxy clusters accrete mass through the aggregation of smaller systems. Thus, the velocity field of the infall regions of clusters contains significant random motion superimposed on radial infall. Because the…
We study motions of galaxies in galaxy clusters formed in the concordance LCDM cosmology. We use high-resolution cosmological simulations that follow dynamics of dark matter and gas and include various physical processes critical for galaxy…
Globular clusters (GCs) are powerful tracers of the galaxy assembly process, and have already been used to obtain a detailed picture of the progenitors of the Milky Way. Using the E-MOSAICS cosmological simulation of a (34.4 Mpc)$^3$ volume…
We discuss a new criterion to estimate the mass in the outer, non-equilibrium region of galaxy clusters, where the galaxy dynamics is dominated by an overall infall motion towards the cluster centre. In the framework of the spherical infall…
The galaxy total mass inside the effective radius encode important information on the dark matter and galaxy evolution model. Total "central" masses can be inferred via galaxy dynamics or with gravitational lensing, but these methods have…
We present a novel approach to identify galaxy clusters that are undergoing a merger using a deep learning approach. This paper uses massive galaxy clusters spanning $0 \leq z \leq 2$ from \textsc{The Three Hundred} project, a suite of…
We present a novel approach for reconstructing the projected mass distribution of clusters of galaxies from sparse and noisy weak gravitational lensing shear data. The reconstructions are regularised using knowledge gained from numerical…