DOI: 10.5281/zenodo.1464831
title: Nbody 3D Histograms dataset

description: <strong>3Dcosmo: a benchmark dataset for large 3-dimensional generative models (and 2-dimensional as well)</strong> This is the N-body simulations 3D images dataset used in the paper <em>Cosmological N-body simulations: a challenge for scalable generative models</em>, Nathanaël Perraudin, Ankit Srivastava, Aurelien Lucchi, Tomasz Kacprzak, Thomas Hofmann, Alexandre Refregier The dataset does not contain the Nbody simulations as they have a very large size. Instead, we sliced the space into 256 x 256 x 256 cubical areas and counted the number of particules in each area. The result are 3D histograms, where the number of particles is a proxy for matter density. <strong>3DCosmo benchmark</strong> This dataset can be used to evaluate 2D and 3D generative model. It is particularly suitable for large scale 3D images. Please check https://github.com/nperraud/3DcosmoGAN for more information. Please consider citing our paper if you use it. <pre><code>@inproceedings{perraudin2019cosmological, title = {Cosmological N-body simulations: a challenge for scalable generative models}, author = {Nathana\"el, Perraudin and Ankit, Srivastava and Kacprzak, Tomasz and Lucchi, Aurelien and Hofmann, Thomas and R{\'e}fr{\'e}gier, Alexandre}, year = {2019}, archivePrefix = {arXiv}, eprint = {1908.05519}, url = {https://arxiv.org/abs/1908.05519}, } </code></pre> While this data is associated to the paper <em>Cosmological N-body simulations: a challenge for scalable generative models</em>, note that a the same Nbody simulation were used in the paper <em>Fast Cosmic Web Simulations with Generative Adversarial Networks</em> (https://arxiv.org/abs/1801.09070v1), but with a different way of building the histogram. You may want to cite this work as well. <pre><code>@article{rodriguez2018fast, title={Fast cosmic web simulations with generative adversarial networks}, author={Rodr{\'\i}guez, Andres C and Kacprzak, Tomasz and Lucchi, Aurelien and Amara, Adam and Sgier, Rapha{\"e}l and Fluri, Janis and Hofmann, Thomas and R{\'e}fr{\'e}gier, Alexandre}, journal={Computational Astrophysics and Cosmology}, volume={5}, number={1}, pages={4}, year={2018}, publisher={Springer} } </code></pre> N-body simulation evolves a cosmological matter distribution over time, starting from soon after the big bang. It represents matter density distribution as a finite set of massive particles, typically order of trillions. The positions of these particles are modified due to gravitational forces and expansion of the cosmological volume due to cosmic acceleration. N-body simulations use periodic boundary condition, where particles leaving the volume on one face enter it back from the opposite side. <strong>Short description of the data generation:</strong> We created N-body simulations of cosmic structures in boxes of size 100 Mpc and 500 Mpc with 512^3 and 1,024^3 particles respectively. We used L-PICOLA [21] to create 10 and 30 independent simulation boxes for both box sizes. The cosmological model used was ΛCDM (Cold Dark Matter) with Hubble constant H0 = 500, h = 350 km s−1 Mpc−1, dark energy density Omega_Lambda = 0.72 and matter density Omega_m = 0.28. We used the particle distribution at redshift z = 0. <br> For additional information, please check the README.md