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Dark Sky Simulations: Early Data Release

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arxiv 1407.2600 v1 pith:6T343XW3 submitted 2014-07-09 astro-ph.CO astro-ph.IM

classification astro-ph.COastro-ph.IM
keywords datadarkmasssimulationsmethodreleaseaccessanalysis
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abstract

The Dark Sky Simulations are an ongoing series of cosmological N-body simulations designed to provide a quantitative and accessible model of the evolution of the large-scale Universe. Such models are essential for many aspects of the study of dark matter and dark energy, since we lack a sufficiently accurate analytic model of non-linear gravitational clustering. In July 2014, we made available to the general community our early data release, consisting of over 55 Terabytes of simulation data products, including our largest simulation to date, which used $1.07 \times 10^{12}~(10240^3)$ particles in a volume $8h^{-1}\mathrm{Gpc}$ across. Our simulations were performed with 2HOT, a purely tree-based adaptive N-body method, running on 200,000 processors of the Titan supercomputer, with data analysis enabled by yt. We provide an overview of the derived halo catalogs, mass function, power spectra and light cone data. We show self-consistency in the mass function and mass power spectrum at the 1% level over a range of more than 1000 in particle mass. We also present a novel method to distribute and access very large datasets, based on an abstraction of the World Wide Web (WWW) as a file system, remote memory-mapped file access semantics, and a space-filling curve index. This method has been implemented for our data release, and provides a means to not only query stored results such as halo catalogs, but also to design and deploy new analysis techniques on large distributed datasets.

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 84 citations worldwide. Full citation record

  1. Machine-assisted Semi-Simulation Model (MSSM): Estimating Galactic Baryonic Properties from their Dark Matter using a Machine Trained on Hydrodynamic Simulations

    astro-ph.GA 2019-08 conditional novelty 6.0 of 10

    An extremely randomized tree model trained on IllustrisTNG predicts baryonic properties from dark matter halos and generates a Gpc-scale catalog compatible with semi-analytic models.

  2. Hydrodynamic methods and sub-resolution models for cosmological simulations

    astro-ph.CO 2025-02 unverdicted novelty 1.0 of 10

    A review chapter summarizes hydrodynamic solvers and subgrid models for cosmological simulations, arguing that the choice of sub-resolution prescriptions materially changes predicted galaxy and cluster properties.

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