Related papers: Finding universal relations in subhalo properties …

Universal approximation theorems provide a mathematical explanation for the expressive power of neural networks. They assert that, under mild conditions on the activation function, feedforward neural networks are dense in broad function…

Large-scale sky surveys require companion large volume simulated mock catalogs. To ensure precision cosmology studies are unbiased, the correlations in these mocks between galaxy properties and their large-scale environments must be…

In simulation-based models of the galaxy-halo connection, theoretical predictions for galaxy clustering and lensing are typically made based on Monte Carlo realizations of a mock universe. In this paper, we use Subhalo Abundance Matching…

We use high-resolution numerical simulations to study the physical properties of subhalos when they merge into their host halos. An improved algorithm is used to identify the subhalos. We then examine their spatial and velocity…

We present a new method for inferring galaxy star formation histories (SFH) using machine learning methods coupled with two cosmological hydrodynamic simulations. We train Convolutional Neural Networks to learn the relationship between…

Strong gravitational lenses enable direct inference of halo abundance and internal structure, which in turn enable constraints on the nature of dark matter and the primordial matter power spectrum. However, the density profiles of dark…

CUSP is a powerful formalism that recovers, from first principles and with no free parameter, all the macroscopic properties of dark matter haloes found in cosmological N-body simulations and unveils the origin of their characteristic…

In this work, we use the theory of spatial networks to analyze galaxy distributions. The aim is to develop new approaches to study the spatial galaxy environment properties by means of the network parameters. We investigate how each of the…

Generative deep learning methods built upon Convolutional Neural Networks (CNNs) provide a great tool for predicting non-linear structure in cosmology. In this work we predict high resolution dark matter halos from large scale, low…

We present a new model to describe the galaxy-dark matter connection across cosmic time, which unlike the popular subhalo abundance matching technique is self-consistent in that it takes account of the facts that (i) subhalos are accreted…

We test and present the application of the full rescaling method by Angulo & White (2010) to change the cosmology of halo catalogues in numerical simulations for cosmological parameter search using semi-analytic galaxy properties. We show…

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…

Dark-matter halos show a universal density profile with a scaling such that less massive systems are typically denser. This mass-density relation is well described by a proportionality between the characteristic density of halos and the…

We combine data from a number of N-body simulations to predict the abundance of dark halos in Cold Dark Matter universes over more than 4 orders of magnitude in mass. A comparison of different simulations suggests that the dominant…

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…

We present the novel wide & deep neural network GalaxyNet, which connects the properties of galaxies and dark matter haloes, and is directly trained on observed galaxy statistics using reinforcement learning. The most important halo…

Using a cosmological N-Body simulation and a sample of re-simulated cluster-like haloes, we study the mass loss rates of dark matter subhaloes, and interpret the mass function of subhaloes at redshift zero in terms of the evolution of the…

Accurate estimation of nuclear masses and their prediction beyond the experimentally explored domains of the nuclear landscape are crucial to an understanding of the fundamental origin of nuclear properties and to many applications of…

A recent comparison of the massive galaxy cluster Abell 2744 with the Millennium XXL (MXXL) N-body simulation has hinted at a tension between the observed substructure distribution and the predictions of LambdaCDM. Follow-up investigations…

Maps of cosmic structure produced by galaxy surveys are one of the key tools for answering fundamental questions about the Universe. Accurate theoretical predictions for these quantities are needed to maximize the scientific return of these…