Related papers: An application of machine learning techniques to g…
Rich and massive clusters of galaxies at intermediate redshift are capable of magnifying and distorting the images of background galaxies. A comparison of different mass estimators among these clusters can provide useful information about…
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…
Strong gravitational lensing by irregular mass distributions, such as galaxy clusters, is generally not well quantified by cross sections of analytic mass models. Computationally expensive ray-tracing methods have so far been necessary for…
Analysing the weak lensing distortions of the images of faint background galaxies provides a means to constrain the average mass distribution of cluster galaxies and potentially to test the extent of their dark matter haloes as a function…
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…
We report weak-lensing masses for 51 of the most X-ray luminous galaxy clusters known. This cluster sample, introduced earlier in this series of papers, spans redshifts 0.15 < z_cl < 0.7, and is well suited to calibrate mass proxies for…
There are only two methods for estimating the mass distribution in the outer regions of galaxy clusters, where virial equilibrium does not hold: weak gravitational lensing and identification of caustics in redshift space. For the first…
The cosmological constraining power of modern galaxy cluster catalogs can be improved by obtaining low-scatter mass proxy measurements for even a small fraction of sources. In the context of large upcoming surveys that will reveal the…
We present the first measurement of the relationship between the Sunyaev-Zel'dovich effect signal and the mass of galaxy clusters that uses gravitational lensing to measure cluster mass, based on 14 X-ray luminous clusters at z~0.2 from the…
The masses of galaxy clusters are commonly measured from X-ray observations under the assumption of hydrostatic equilibrium (HSE). This technique is known to underestimate the true mass systematically. The fiducial FLAMINGO cosmological…
A tight mass-temperature relation, M(r)/r ~ T_X, is expected in most cosmological models if clusters of galaxies are homologous and the intracluster gas is in global equilibrium with the dark matter. We here calibrate this relation using 8…
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 propose a random forest (RF) machine learning approach to determine the accreted stellar mass fractions ($f_\mathrm{acc}$) of central galaxies, based on various dark matter halo and galaxy features. The RF is trained and tested using…
The rise of Big Data has led to new demands for Machine Learning (ML) systems to learn complex models with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics…
We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical…
The combination of galaxy-galaxy lensing (GGL) with galaxy clustering is one of the most promising routes to determining the amplitude of matter clustering at low redshifts. We show that extending clustering+GGL analyses from the linear…
We present a new non-parametric method for determining mean 3D density and mass profiles from weak lensing measurements around stacked samples of galaxies or clusters, that is, from measurement of the galaxy-shear or cluster-shear…
Machine learning (ML) has often been applied to space weather (SW) problems in recent years. SW originates from solar perturbations and is comprised of the resulting complex variations they cause within the systems between the Sun and…
We apply the capabilities of machine learning (ML) to discern patterns in order to classify metal-poor stars. To do so, we train an ML model on a bank of nucleosynthesis calculations derived from hydrodynamic simulations for events such as…
The estimation of the bulge and disk massses, the main baryonic components of a galaxy, can be performed using various approaches, but their implementation tend to be challenging as they often rely on strong assumptions about either the…