340 citations · 1.4k across the 15 of their papers we have counts for
10 papers · 1 filter
The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues
T. Lucas Makinen, Tom Charnock, Pablo Lemos +3
We present an implicit likelihood approach to quantifying cosmological information over discrete catalogue data, assembled as graphs. To do so, we explore cosmological parameter co…
Lossless, Scalable Implicit Likelihood Inference for Cosmological Fields
T. Lucas Makinen, Tom Charnock, Justin Alsing +1
We present a comparison of simulation-based inference to full, field-based analytical inference in cosmological data analysis. To do so, we explore parameter inference for two case…
Super-resolution emulator of cosmological simulations using deep physical models
Doogesh Kodi Ramanah, Tom Charnock, Francisco Villaescusa-Navarro +1
We present an extension of our recently developed Wasserstein optimized model to emulate accurate high-resolution features from computationally cheaper low-resolution cosmological…
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…
Planck confronts large scale structure: methods to quantify discordance
Tom Charnock, Richard A. Battye, Adam Moss
Discordance in the CDM cosmological model can be seen by comparing parameters constrained by CMB measurements to those inferred by probes of large scale structure. Recent improv…
CMB constraints on cosmic strings and superstrings
Tom Charnock, Anastasios Avgoustidis, Edmund J. Copeland +1
We present the first complete Markov chain Monte Carlo analysis of cosmological models with evolving cosmic (super)string networks, using the unconnected segment model in the unequ…
Tension between the power spectrum of density perturbations measured on large and small scales
Richard A. Battye, Tom Charnock, Adam Moss
There is a tension between measurements of the amplitude of the power spectrum of density perturbations inferred using the Cosmic Microwave Background (CMB) and directly measured b…