340 citations · 1.4k across the 15 of their papers we have counts for
4 papers · 1 filter
Neural physical engines for inferring the halo mass distribution function
Tom Charnock, Guilhem Lavaux, Benjamin D. Wandelt +3
An ambitious goal in cosmology is to forward-model the observed distribution of galaxies in the nearby Universe today from the initial conditions of large-scale structures. For pra…
The Quijote simulations
Francisco Villaescusa-Navarro, ChangHoon Hahn, Elena Massara +26
The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7,000 cosmological models in the hyperplane.…
Painting halos from cosmic density fields of dark matter with physically motivated neural networks
Doogesh Kodi Ramanah, Tom Charnock, Guilhem Lavaux
We present a novel halo painting network that learns to map approximate 3D dark matter fields to realistic halo distributions. This map is provided via a physically motivated netwo…
Fast likelihood-free cosmology with neural density estimators and active learning
Justin Alsing, Tom Charnock, Stephen Feeney +1
Likelihood-free inference provides a framework for performing rigorous Bayesian inference using only forward simulations, properly accounting for all physical and observational eff…