Related papers: Classification algorithms applied to structure for…
We present an analysis of the behaviour of the `coarse-grained' (`mesoscopic') rank partitioning of the mean energy of collections of particles composing virialized dark matter halos in a Lambda-CDM cosmological simulation. We find evidence…
One of the most challenging open questions in physics today is discovering the nature of dark matter. In this work we study the imaging formation in dark matter (DM) halos due to an external light source using some DM profiles for…
The identity of dark matter remains one of the most pressing questions in physics today. While many promising dark matter candidates have been put forth over the last half-century, to date the true identity of dark matter remains elusive.…
We assess the effectiveness of a non-parametric bias model in generating mock halo catalogues for modified gravity (MG) cosmologies, relying on the distribution of dark matter from either MG or $\Lambda$CDM. We aim to generate halo…
A grand challenge of the 21st century cosmology is to accurately estimate the cosmological parameters of our Universe. A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe.…
We present a method for populating dark matter simulations with haloes of mass below the resolution limit. It is based on stochastically sampling a field derived from the density field of the halo catalogue, using constraints from the…
We use an extremely large volume ($2.4h^{-3}{\rm Gpc}^{3}$), high resolution N-body simulation to measure the higher order clustering of dark matter haloes as a function of mass and internal structure. As a result of the large simulation…
We derive an analytic formula of genus for dark matter halos assuming that the primordial mass density field obeys the random-Gaussian statistics. In particular, we for the first time take account of the nonlinear nature of the halo…
We review the current state of the theory of large scale structure in a warm dark matter (WDM) cosmological model. In particular, we focus on the non-linear modelling of the matter power spectrum and on the mass function of dark matter…
As a flexible nonparametric learning tool, the random forests algorithm has been widely applied to various real applications with appealing empirical performance, even in the presence of high-dimensional feature space. Unveiling the…
With future wide and deep cosmological sky surveys, a large number of gravitationally lensed, multiply imaged systems will be found. In addition to multiply imaged galaxies and quasars, sources will include transient events like supernovae…
For nearly 40 years, dark matter has been widely assumed to be cold and collisionless. Cold dark matter models make fundamental predictions for the behavior of dark matter on small (<10 kpc) scales. These predictions include cuspy density…
We review the field of collisionless numerical simulations for the large-scale structure of the Universe. We start by providing the main set of equations solved by these simulations and their connection with General Relativity. We then…
Scaling relations among dark matter (DM) and stellar quantities are a valuable tool to constrain formation scenarios and the evolution of galactic structures. However, most of the DM properties are actually not directly measured, but…
A method of deriving and using merging history trees of dark matter galaxy haloes directly from pure gravity N-body simulations is presented. This combines the full non-linearity of N-body simulations with the flexibility of the…
The concentrations of the cuspy dark matter halos predicted by simulations of cold dark matter are related to the cosmology in which the halos form. Observational constraints on halo concentration therefore map into constraints on…
We study the effects of the local environmental density and the cosmic web environment (filaments, walls, and voids) on key properties of dark matter halos using the Bolshoi-Planck LCDM cosmological simulation. The z = 0 simulation is…
The statistical properties of dark matter halos, the building blocks of cosmological observables associated with structure in the universe, offer many opportunities to test models for cosmic acceleration, especially those that seek to…
Random Forest has become one of the most popular tools for feature selection. Its ability to deal with high-dimensional data makes this algorithm especially useful for studies in neuroimaging and bioinformatics. Despite its popularity and…
Strong gravitational lensing is a promising probe of the substructure of dark matter halos. Deep learning methods have the potential to accurately identify images containing substructure, and differentiate WIMP dark matter from other well…