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Dataset Open Access

Nbody 3D Histograms dataset

Janis Fluri; Nathanael Perraudin


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        <foaf:name>Janis Fluri</foaf:name>
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        <foaf:name>Nathanael Perraudin</foaf:name>
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    <dct:title>Nbody 3D Histograms dataset</dct:title>
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    <dct:description>&lt;p&gt;&lt;br&gt; This is the N-body simulations 3D images dataset used in the following paper:&lt;br&gt; &lt;strong&gt;Scalable Generative Adversarial Networks for Multi-dimensional Images&lt;/strong&gt;&lt;br&gt; &lt;em&gt;Ankit Srivastava, Nathana&amp;euml;l Perraudin, Aurelien Lucchi, Tomasz Kacprzak, Thomas Hofmann, Alexandre Refregier, Adam Amara&lt;/em&gt;&lt;br&gt; &lt;a href="https://arxiv.org/abs/1908.05519"&gt;https://arxiv.org/abs/1908.05519&lt;/a&gt;&lt;/p&gt; &lt;blockquote&gt; &lt;p&gt;@inproceedings{perraudin2019cosmological,&lt;br&gt; &amp;nbsp; title = {Cosmological N-body simulations: a challenge for scalable generative models},&lt;br&gt; &amp;nbsp; author = {Nathana\&amp;quot;el, Perraudin and Ankit, Srivastava and Kacprzak, Tomasz and Lucchi, Aurelien and Hofmann, Thomas and R{\&amp;#39;e}fr{\&amp;#39;e}gier, Alexandre},&lt;br&gt; &amp;nbsp; year = {2019},&lt;br&gt; &amp;nbsp; archivePrefix = {arXiv},&lt;br&gt; &amp;nbsp; eprint = {1908.05519},&lt;br&gt; &amp;nbsp; url = {https://arxiv.org/abs/1908.05519},&lt;br&gt; }&lt;/p&gt; &lt;/blockquote&gt; &lt;p&gt;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.&lt;/p&gt; &lt;p&gt;If you work with this dataset, you might be interested in this code as well &lt;a href="https://github.com/nperraud/3DcosmoGAN"&gt;https://github.com/nperraud/3DcosmoGAN&lt;/a&gt;&lt;/p&gt; &lt;p&gt;Note that a the same Nbody simulation were used in this paper, but with a different way of building the histogram.&lt;br&gt; &lt;strong&gt;Fast Cosmic Web Simulations with Generative Adversarial Networks&lt;/strong&gt;&lt;br&gt; &lt;em&gt;Andres C Rodriguez, Tomasz Kacprzak, Aurelien Lucchi, Adam Amara, Raphael Sgier, Janis Fluri, Thomas Hofmann, Alexandre R&amp;eacute;fr&amp;eacute;gier&lt;/em&gt;&lt;br&gt; &lt;a href="https://arxiv.org/abs/1801.09070v1"&gt;https://arxiv.org/abs/1801.09070v1&lt;/a&gt;&lt;/p&gt; &lt;p&gt;&lt;br&gt; N-body simulation evolves a cosmological matter distribution over time, starting from soon after the big bang.&lt;br&gt; It represents matter density distribution as a finite set of massive particles, typically order of trillions.&lt;br&gt; The positions of these particles are modified due to gravitational forces and expansion of the cosmological volume due to cosmic acceleration.&lt;br&gt; N-body simulations use periodic boundary condition, where particles leaving the volume on one face enter it back from the opposite side.&lt;/p&gt; &lt;p&gt;## Short description of the data generation from Rordiguez et al. 2018:&lt;/p&gt; &lt;p&gt;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.&lt;br&gt; We used L-PICOLA [21] to create 10 and 30 independent simulation boxes for both box sizes.&lt;br&gt; The cosmological model used was &amp;Lambda;CDM (Cold Dark Matter) with Hubble constant H0 = 100, h = 70 km s&amp;minus;1 Mpc&amp;minus;1,&lt;br&gt; dark energy density Omega_Lambda = 0.72 and matter density Omega_m = 0.28.&lt;br&gt; We used the particle distribution at redshift z = 0.&lt;/p&gt; &lt;p&gt;&lt;br&gt; For additional information, please check the README.md&lt;/p&gt;</dct:description>
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