The Quijote Simulations

By Francisco Villaescusa-Navarro, C. Hahn, E. Massara, Arka Banerjee, Ana Maria Delgado, D. K. Ramanah, Tom Charnock, E. Giusarma, Yin Li, E. Allys and 17 more. Astrophysical Journal Supplement Series, 2019

273 citations recorded by the source corpus.

The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7000 cosmological models in the hyperplane. At a single redshift, the simulations contain more than 8.5 trillion particles over a combined volume of 44,100 each simulation follows the evolution of 2563, 5123, or 10243 particles in a box of 1 h−1 Gpc length. Billions of dark matter halos and cosmic voids have been identified in the simulations, whose runs required more than 35 million core hours. The Quijote simulations have been designed for two main purposes: (1) to quantify the information content on cosmological observables and (2) to provide enough data to train machine-learning algorithms. In this paper, we describe the simulations and show a few of their applications. We also release the petabyte of data generated, comprising hundreds of thousands of simulation snapshots at multiple redshifts; halo and void catalogs; and millions of summary statistics, such as power spectra, bispectra, correlation functions, marked power spectra, and estimated probability density functions.

Subfields: Astronomy and Astrophysics, Instrumentation

View Paper: https://www.semanticscholar.org/paper/425e12a000670bf7cea49ae175d809267d04382f

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Citations in the corpus

Discovering Symbolic Models from Deep Learning with Inductive Biases (2020, methodology): “We study this problem with the open sourced N-body dark matter simulations from [40].”