777 citations · 1.8k across the 46 of their papers we have counts for
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Learning cosmic web environments with diffusion models
Mehdi Noor, Tony Bonnaire, Nabila Aghanim +1
The cosmic web, consisting of an intricate network of voids, walls, filaments, and nodes, encodes key information about structure formation and the cosmological parameters that gov…
Predicting large scale cosmological structure evolution with generative adversarial network-based autoencoders
Marion Ullmo, Nabila Aghanim, Aurélien Decelle +1
Predicting the nonlinear evolution of cosmic structure from initial conditions is typically approached using Lagrangian, particle-based methods. These techniques excel in terms of…
Cosmology with cosmic web environments II. Redshift-space auto and cross power spectra
Tony Bonnaire, Joseph Kuruvilla, Nabila Aghanim +1
Degeneracies among parameters of the cosmological model are known to drastically limit the information contained in the matter distribution. In the first paper of this series, we s…
Cosmology with cosmic web environments I. Real-space power spectra
Tony Bonnaire, Nabila Aghanim, Joseph Kuruvilla +1
We undertake the first comprehensive and quantitative real-space analysis of the cosmological information content in the environments of the cosmic web (voids, filaments, walls, an…
Encoding large scale cosmological structure with Generative Adversarial Networks
Marion Ullmo, Aurélien Decelle, Nabila Aghanim
Recently a type of neural networks called Generative Adversarial Networks (GANs) has been proposed as a solution for fast generation of simulation-like datasets, in an attempt to b…