Related papers: Estimating Cosmological Parameters from the Dark M…
Propagation of light in the universe with structure which amplify and modify the shape of distant galaxies, producing a correlation between nearby and distant density of galaxies, is a phenomena very important in cosmology for determining…
Observations of the clustering of galaxies can provide useful information about the distribution of dark matter in the Universe. In order to extract accurate cosmological parameters from galaxy surveys, it is important to understand how the…
The large-scale structure in cosmology is highly non-Gaussian at late times and small length scales, making it difficult to describe analytically. Parameter inference, data reconstruction, and data generation tasks in cosmology are greatly…
We perform an analysis of the Cosmic Web as a complex network, which is built on a $\Lambda$CDM cosmological simulation. For each of nodes, which are in this case dark matter halos formed in the simulation, we compute 10 network metrics,…
We propose a light-weight deep convolutional neural network (CNN) to estimate the cosmological parameters from simulated 3-dimensional dark matter distributions with high accuracy. The training set is based on 465 realizations of a cubic…
The spatial arrangement of galaxies (of satellites on a scale of 100kpc) as well as their three-dimensional distribution in galaxy groups such as the Local Group (on a scale of 1Mpc), the distribution of galaxies in the nearby volume of…
The possibility to constrain cosmological parameters from galaxy surveys using field-level machine learning methods that bypass traditional summary statistics analyses, depends crucially on our ability to generate simulated training sets.…
In recent years, deep learning approaches have achieved state-of-the-art results in the analysis of point cloud data. In cosmology, galaxy redshift surveys resemble such a permutation invariant collection of positions in space. These…
Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning, which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions…
We discuss the use of galaxies to trace the large-scale structure of the universe and thereby to make cosmological inferences. We put special emphasis on our lack of knowledge about the relative distribution of galaxies and the dynamically…
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…
The cosmic web that characterizes the large-scale structure of the Universe can be quantified by a variety of methods. For example, large redshift surveys can be used in combination with point process algorithms to extract long curvilinear…
The estimation of cosmological parameters from precision observables is an important industry with crucial ramifications for particle physics. This article discusses the statistical methods presently used in cosmological data analysis,…
We review how dark matter is distributed in our local neighbourhood from an observational and theoretical perspective. We will start by describing first the dark matter halo of our own galaxy and in the Local Group. Then we proceed to…
Weak gravitational lensing is responsible for the shearing and magnification of the images of high-redshift sources due to the presence of intervening matter. The distortions are due to fluctuations in the gravitational potential, and are…
With the developments of large galaxy surveys or cosmic shear surveys it is now possible to map the dark matter distribution at truly cosmological scales. Detailed examinations of the statistical properties of the dark matter distribution…
A simple speed-up cosmology model is proposed to account for the dark energy puzzle. We condense contributions from dark energy and curvature term into one effective parameter in order to reduce parameter degeneracies and to find any…
Weak gravitational lensing provides a unique method to map directly the distribution of dark matter in the universe and to measure cosmological parameters. This cosmic-shear technique is based on the measurement of the weak distortions that…
We investigate how observations of strong lensing can be used to infer cosmological parameters, in particular the equation of state of dark energy. We focus on the growth of the critical lines of lensing clusters with the source redshift as…
The large-scale structure is a major source of cosmological information. However, next-generation photometric galaxy surveys will only provide a distorted view of cosmic structures due to large redshift uncertainties. To address the need…