Published September 1, 2020 | Version v1
Journal article

The Quijote Simulations

  • 1. Center for Computational Astrophysics, Flatiron Institute, 162 5th Avenue, New York, NY 10010 (United States)
  • 2. Department of Physics, University of California, Berkeley, CA 94720 (United States)
  • 3. Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, 452 Lomita Mall, Stanford, CA 94305 (United States)
  • 4. Sorbonne Universite, CNRS, UMR 7095, Institut d'Astrophysique de Paris, 98 bis boulevard Arago, F-75014 Paris (France)
  • 5. Laboratoire de Physique de l'Ecole normale superieure, ENS, Universite PSL, CNRS, Paris (France)
  • 6. INRIA, ENS, PSL Research University Paris (France)
  • 7. Centre for Theoretical Cosmology, DAMTP, University of Cambridge, CB3 0WA Cambridge (United Kingdom)
  • 8. Physics Department, Brookhaven National Laboratory, Upton, NY 11973 (United States)
  • 9. Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton, NJ 08544-0010 (United States)
  • 10. Waterloo Centre for Astrophysics, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1 (Canada)

Description

The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7000 cosmological models in the { Ω m , Ω b , h , n s , σ 8 , M ν , w } hyperplane. At a single redshift, the simulations contain more than 8.5 trillion particles over a combined volume of 44,100 ( h 1 G p c ) 3 ; 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.

Availability note (English)

Available from http://dx.doi.org/10.3847/1538-4365/ab9d82

Additional details

Identifiers

Publishing Information

Journal Title
Astrophysical Journal. Supplement Series
Journal Volume
250
Journal Issue
1
Journal Page Range
[20 p.]
ISSN
0067-0049
CODEN
APJSA2

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
52057466
Subject category
S79: ASTROPHYSICS, COSMOLOGY AND ASTRONOMY;
Descriptors DEI
CORRELATION FUNCTIONS; COSMOLOGICAL MODELS; MACHINE LEARNING; NONLUMINOUS MATTER; RED SHIFT; SIMULATION; SPECTRA
Descriptors DEC
ALGORITHMS; ARTIFICIAL INTELLIGENCE; FUNCTIONS; LEARNING; MATHEMATICAL LOGIC; MATHEMATICAL MODELS; MATTER