Encoding large scale cosmological structure with Generative Adversarial Networks

Understand

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 bypass heavy computations and expensive cosmological simulations to run in terms of time and computing power.

• In the present work, we build and train a GAN to look further into the strengths and limitations of such an approach.
• We then propose a novel method in which we make use of a trained GAN to construct a simple autoencoder (AE) as a first step towards building a predictive model.
• Both the GAN and AE are trained on images issued from two types of N-body simulations, namely 2D and 3D simulations.