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The Quijote simulations
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abstract
The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7,000 cosmological models in the $\{\Omega_{\rm m}, \Omega_{\rm b}, h, n_s, \sigma_8, M_\nu, w \}$ hyperplane. At a single redshift the simulations contain more than 8.5 trillions of particles over a combined volume of 44,100 $(h^{-1}{\rm Gpc})^3$; each simulation follow the evolution of $256^3$, $512^3$ or $1024^3$ particles in a box of $1~h^{-1}{\rm Gpc}$ 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, together with millions of summary statistics such as power spectra, bispectra, correlation functions, marked power spectra, and estimated probability density functions.
Forward citations
Cited by 13 Pith papers
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Primordial Physics in the Nonlinear Universe: Revealing the oscillating halo bias from cosmological collider models
A binning-based IC method yields the first N-body measurements of oscillating halo bias from cosmological collider bispectra, with mass- and assembly-dependent phases fit by peak-background-split theory.
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Weighted Webs: Morphology-Informed Marked Fields
Morphology-based marks (tidal shear and local fractal dimension) add a modest but complementary ~10% Fisher-information gain over density-only marked power spectra for cosmological parameters.
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Hermes - Towards an Optimal High-Performance Algorithm for Cosmic Statistics of Large Data Sets
Hermes/PyHermes reconstructs catalogues in a scaling-function basis and unifies CIC, 2PCF, 3PCF, marked, and operator-based cosmic statistics as reusable window operations with FFT/MPI/GPU scaling.
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Floquet Dissipative Phase Transitions
Dissipative phase transitions in Floquet open systems are defined through the Floquet propagator spectrum, with applications to driven Kerr resonators and the quantum Rabi model.
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Emulating galaxy and peculiar velocity clustering on non-linear scales
Adding peculiar-velocity clustering to galaxy clustering on non-linear scales tightens forecast fσ8 constraints from 4.7% to 3.8% in realistic mocks, but biases the recovered HOD parameters.
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PatchNet: A hierarchical approach for neural field-level inference from Quijote Simulations
Combining patch-level neural summaries with power spectrum and bispectrum extracts roughly as much cosmological information from dark matter simulations as wavelet statistics, apparently nearing the information limit ...
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Differentiable Halo Mass Prediction and the Cosmology-Dependence of Halo Mass Functions
A differentiable U-Net predicts halo mass functions and their cosmology derivatives from initial density fields, matching finite-difference gradients of simulations and emulators to within model scatter.
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CSST Cosmological Emulator II: Generalized Accurate Halo Mass Function Emulation
A new emulator predicts cumulative dark matter halo mass functions for three mass definitions with claimed 2-10% accuracy from z=0 to 3, based on the Kun simulation suite.
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Computing Nonlinear Power Spectra Across Dynamical Dark Energy Model Space with Neural ODEs
A neural ODE trained only on LambdaCDM spectra predicts nonlinear matter power spectra to about 4 percent accuracy for smooth w(z) dark energy models, pending stronger validation.
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Bispectrum BAO and the baryon-dark matter relative velocity
A template-based bispectrum-monopole BAO measurement is shown to be unbiased on simulations and, jointly with the power spectrum, to sharpen BAO constraints while exposing baryon-dark matter velocity biases.
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The Density Profile of Dynamical Halos
For dynamical dark matter halos, the orbiting density profile at fixed mass is set by one scale, the halo radius, whose scatter shrinks from 16% to 11% when formation time is included.
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Implicit Likelihood Inference of the Neutrino Mass Hierarchy from Cosmological Data
A simulation-based neural-likelihood analysis of Planck 2018 and DESI DR2 data reports a weak preference (tilde_Delta = 0.12, 68% CL interval spanning both signs) for the normal neutrino mass hierarchy.
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GGI Lectures on Large-Scale Structure Perturbation Theory (Effective Field Theory)
Pedagogical notes derive large-scale-structure EFT from symmetries, covering SPT failures, BAO IR resummation, counterterms, galaxy bias, redshift-space distortions, and Lagrangian PT.
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