340 citations · 1.4k across the 15 of their papers we have counts for
16 papers
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…
Catalog-free modeling of galaxy types in deep images: Massive dimensional reduction with neural networks
Florian Livet, Tom Charnock, Damien Le Borgne +1
Current models of galaxy evolution are constrained by the analysis of catalogs containing the flux and size of galaxies extracted from multiband deep fields carrying inevitable obs…
Bayesian Neural Networks
Tom Charnock, Laurence Perreault-Levasseur, François Lanusse
In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models. However, their introduction intrinsically increases our uncertain…
Detecting outliers in astronomical images with deep generative networks
Berta Margalef-Bentabol, Marc Huertas-Company, Tom Charnock +5
With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative net…
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…