Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

01-12-2019 | Research | Issue 1/2019 Open Access

Computational Astrophysics and Cosmology 1/2019

Cosmological N-body simulations: a challenge for scalable generative models

Journal:
Computational Astrophysics and Cosmology > Issue 1/2019
Authors:
Nathanaël Perraudin, Ankit Srivastava, Aurelien Lucchi, Tomasz Kacprzak, Thomas Hofmann, Alexandre Réfrégier
Important notes

Electronic Supplementary Material

The online version of this article (https://​doi.​org/​10.​1186/​s40668-019-0032-1) contains supplementary material.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Deep generative models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAs) have been demonstrated to produce images of high visual quality. However, the existing hardware on which these models are trained severely limits the size of the images that can be generated. The rapid growth of high dimensional data in many fields of science therefore poses a significant challenge for generative models. In cosmology, the large-scale, three-dimensional matter distribution, modeled with N-body simulations, plays a crucial role in understanding the evolution of structures in the universe. As these simulations are computationally very expensive, GANs have recently generated interest as a possible method to emulate these datasets, but they have been, so far, mostly limited to two dimensional data. In this work, we introduce a new benchmark for the generation of three dimensional N-body simulations, in order to stimulate new ideas in the machine learning community and move closer to the practical use of generative models in cosmology. As a first benchmark result, we propose a scalable GAN approach for training a generator of N-body three-dimensional cubes. Our technique relies on two key building blocks, (i) splitting the generation of the high-dimensional data into smaller parts, and (ii) using a multi-scale approach that efficiently captures global image features that might otherwise be lost in the splitting process. We evaluate the performance of our model for the generation of N-body samples using various statistical measures commonly used in cosmology. Our results show that the proposed model produces samples of high visual quality, although the statistical analysis reveals that capturing rare features in the data poses significant problems for the generative models. We make the data, quality evaluation routines, and the proposed GAN architecture publicly available at https://​github.​com/​nperraud/​3DcosmoGAN.

Our product recommendations

Premium-Abo der Gesellschaft für Informatik

Sie erhalten uneingeschränkten Vollzugriff auf alle acht Fachgebiete von Springer Professional und damit auf über 45.000 Fachbücher und ca. 300 Fachzeitschriften.

Supplementary Material
Video Fig. 5 (MP4 72.6 MB)
Video Fig. 7 (MP4 46.3 MB)
Video Fig. 9 (MOV 172.7 MB)
Video Fig. 11 (MOV 335.0 MB)
Video Fig. 14 (MOV 335.0 MB)
Literature
About this article

Other articles of this Issue 1/2019

Computational Astrophysics and Cosmology 1/2019 Go to the issue

Premium Partner

    Image Credits