Intelligent Design: On the Emulation of Cosmological Simulations

Abstract

Simulation design is the choice of locations in parameter space at which simulations are to be run and is the first step in building an emulator capable of quickly providing estimates of simulation results for arbitrary locations in the parameter space. We introduce an alteration to the "OALHS" design used by Heitmann et al. (2006) that reduces the number of simulation runs required to achieve a fixed accuracy in our case study by a factor of two. We also compare interpolation procedures for emulators and find that interpolation via Gaussian Process models and via the much-easier-to-implement polynomial interpolation have comparable accuracy. A very simple emulation-building procedure consisting of a design sampled from the parameter prior distribution, combined with interpolation via polynomials also performs well. Although our primary motivation is efficient emulators of non-linear cosmological N-body simulations, in an appendix we describe an emulator for the CMB temperature power spectrum publicly available as computer code.

13 pages, 13 figures, Matches version to be published in ApJ. Emulator code available at http://www.emucmb.info

References in corpus (12)

• Five-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Angular Power Spectra
• The Coyote Universe III: Simulation Suite and Precision Emulator for the Nonlinear Matter Power Spectrum
• One simulation to fit them all - changing the background parameters of a cosmological N-body simulation
• The Parameter Space of Galaxy Formation
• Cosmic Calibration: Constraints from the Matter Power Spectrum and the Cosmic Microwave Background
• Cosmological and astrophysical constraints from the Lyman-alpha forest flux probability distribution function
• Cosmic Calibration
• Large scale bias and the inaccuracy of the peak-background split
• Fast cosmological parameter estimation using neural networks
• {\sc CosmoNet}: fast cosmological parameter estimation in non-flat models using neural networks
• Simulations and cosmological inference: A statistical model for power spectra means and covariances
• Pico: Parameters for the Impatient Cosmologist

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• PkANN - I. Non-linear matter power spectrum interpolation through artificial neural networks
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• The one-loop matter bispectrum as a probe of gravity and dark energy
• CosmicNet II: Emulating extended cosmologies with efficient and accurate neural networks
• Kernel-Based Emulator for the 3D Matter Power Spectrum from CLASS
• Machine Learning for Observational Cosmology
• On estimating cosmology-dependent covariance matrices
• Parameter Inference for Weak Lensing using Gaussian Processes and MOPED
• Learn-As-You-Go Acceleration of Cosmological Parameter Estimates
• Misinterpreting Modified Gravity as Dark Energy: a Quantitative Study
• Foreground Predictions for the Cosmic Microwave Background Power Spectrum from Measurements of Faint Inverted Radio Sources at 5 GHz