Dark Sky Simulations: Early Data Release

Samuel W. Skillman, Michael S. Warren, Matthew J. Turk, Risa H. Wechsler, Daniel E. Holz, P. M. Sutter
Kavli Institute for Particle Astrophysics and Cosmology, P.O. Box 2450, Stanford, CA 94305, USA
arXiv:1407.2600 [astro-ph.CO], (9 Jul 2014)


   author={Skillman}, S.~W. and {Warren}, M.~S. and {Turk}, M.~J. and {Wechsler}, R.~H. and {Holz}, D.~E. and {Sutter}, P.~M.},

   title={"{Dark Sky Simulations: Early Data Release}"},

   journal={ArXiv e-prints},



   keywords={Astrophysics – Cosmology and Nongalactic Astrophysics, Astrophysics – Instrumentation and Methods for Astrophysics},




   adsnote={Provided by the SAO/NASA Astrophysics Data System}


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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 1.07×10^12 (10240^3) particles in a volume 8h^-1Gpc 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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