The ART of Cosmological Simulations

Gottlöber, Stefan; Klypin, Anatoly

doi:10.1007/978-3-540-69182-2_3

Cited by 4 publications

(4 citation statements)

References 18 publications

(21 reference statements)

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“…The Bolshoi simulation has been performed using the Adaptive Refinement Tree (ART) code (Kravtsov et al 1997). The code was parallelized using MPI libraries and OpenMP directives (Gottlöber & Klypin 2009). The simulation is described in detail in ).…”

Section: Simulation Datamentioning

confidence: 99%

The MultiDark Database: Release of the Bolshoi and MultiDark cosmological simulations

et al. 2013

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We present the online MultiDark Database -a Virtual Observatory-oriented, relational database for hosting various cosmological simulations. The data is accessible via an SQL (Structured Query Language) query interface, which also allows users to directly pose scientific questions, as shown in a number of examples in this paper. Further examples for the usage of the database are given in its extensive online documentation. The database is based on the same technology as the Millennium Database, a fact that will greatly facilitate the usage of both suites of cosmological simulations. The first release of the MultiDark Database hosts two 8.6 billion particle cosmological N-body simulations: the Bolshoi (250 h −1 Mpc simulation box, 1 h −1 kpc resolution) and MultiDark Run1 simulation (MDR1, or BigBolshoi, 1000 h −1 Mpc simulation box, 7 h −1 kpc resolution). The extraction methods for halos/subhalos from the raw simulation data, and how this data is structured in the database are explained in this paper. With the first data release, users get full access to halo/subhalo catalogs, various profiles of the halos at redshifts z = 0-15, and raw dark matter data for one time-step of the Bolshoi and four time-steps of the MultiDark simulation. Later releases will also include galaxy mock catalogs and additional merger trees for both simulations as well as new large volume simulations with high resolution. This project is further proof of the viability to store and present complex data using relational database technology. We encourage other simulators to publish their results in a similar manner.

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Section: Simulation Datamentioning

confidence: 99%

The MultiDark Database: Release of the Bolshoi and MultiDark cosmological simulations

et al. 2013

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“…The simulation we use is the "L1000W" presented in Tinker et al (2008Tinker et al ( , 2010, where the halo mass function and bias function are calibrated. It evolves 1024 3 particles with mp = 6.98 × 10 10 h −1 M⊙ in a periodic box of co-moving length 1000h −1 Mpc using the Adaptive Refinement Tree (ART; Kravtsov et al 1997;Gottloeber & Klypin 2008) code. For the mass scales we consider here, M > 5 × 10 13 h −1 M⊙, it has at least ∼ 1000 particles for each halo and is thus well-suited for the study of ξ hm and ∆Σ.…”

Section: Halo-mass Correlation Functionmentioning

confidence: 99%

Cosmological constraints from the large-scale weak lensing of SDSS MaxBCG clusters

Zu¹,

Weinberg²,

Rozo³

et al. 2014

Monthly Notices of the Royal Astronomical Society

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We derive constraints on the matter density Ω m and the amplitude of matter clustering σ 8 from measurements of large scale weak lensing (projected separation R = 5 − 30h −1 Mpc) by clusters in the Sloan Digital Sky Survey MaxBCG catalog. The weak lensing signal is proportional to the product of Ω m and the cluster-mass correlation function ξ cm . With the relation between optical richness and cluster mass constrained by the observed cluster number counts, the predicted lensing signal increases with increasing Ω m or σ 8 , with mild additional dependence on the assumed scatter between richness and mass. The dependence of the signal on scale and richness partly breaks the degeneracies among these parameters. We incorporate external priors on the richness-mass scatter from comparisons to X-ray data and on the shape of the matter power spectrum from galaxy clustering, and we test our adopted model for ξ cm against N-body simulations. Using a Bayesian approach with minimal restrictive priors, we find σ 8 (Ω m /0.325) 0.501 = 0.828 ± 0.049, with marginalized constraints of Ω m = 0.325 +0.086 −0.067 and σ 8 = 0.828 +0.111 −0.097 , consistent with constraints from other MaxBCG studies that use weak lensing measurements on small scales (R 2h −1 Mpc). The (Ω m , σ 8 ) constraint is consistent with and orthogonal to the one inferred from WMAP CMB data, reflecting agreement with the structure growth predicted by General Relativity for a ΛCDM cosmological model. A joint constraint assuming ΛCDM yields Ω m = 0.298 +0.019 −0.020 and σ 8 = 0.831 +0.020 −0.020 . For these parameters and our best-fit scatter we obtain a tightly constrained mean richness-mass relation of MaxBCG clusters, N 200 = 25.4(M/3.61×10 14 h −1 M ⊙ ) 0.74 , with a normalization uncertainty of 1.5% Our cosmological parameter errors are dominated by the statistical uncertainties of the large scale weak lensing measurements, which should shrink sharply with current and future imaging surveys.

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“…As we want to study γ -rays from large structures in the Local Universe such as nearby galaxy clusters, we choose the Box160CR simulation. This is a constrained realization with 1024 3 particles in a cube of 160 h −1 Mpc on a side which was run using the MPI-ART cosmological code (Kravtsov et al 1997;Gottlöber & Klypin 2008). The initial conditions are set assuming WMAP3 cosmology (with Ω m = 0.24, Ω Λ = 0.76, Ω b = 0.042, h = 0.73, σ 8 = 0.75, and n = 0.95) and implement the constraints from the observed density field so that it reproduces the observed matter distribution in the local universe on large scales at redshift z = 0 (Hoffman & Ribak 1991;Klypin et al 2003).…”

Section: Constrained Simulations Of the Local Universementioning

confidence: 99%

DARK MATTER DECAY AND ANNIHILATION IN THE LOCAL UNIVERSE: CLUES FROM FERMI

Cuesta

Jeltema

Zandanel

et al. 2010

ApJ

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We present all-sky simulated Fermi maps of γ -rays from dark matter (DM) decay and annihilation in the local universe. The DM distribution is obtained from a constrained cosmological simulation of the neighboring large-scale structure provided by the CLUES project. The DM fields of density and density squared are then taken as an input for the Fermi observation simulation tool to predict the γ -ray photon counts that Fermi would detect in 5 years of an all-sky survey for given DM models. Signal-to-noise ratio (S/N) sky maps have also been obtained by adopting the current Galactic and isotropic diffuse background models released by the Fermi Collaboration. We point out the possibility for Fermi to detect a DM γ -ray signal in local extragalactic structures. In particular, we conclude here that Fermi observations of nearby clusters (e.g., Virgo and Coma) and filaments are expected to give stronger constraints on decaying DM compared to previous studies. As an example, we find a significant S/N in DM models with a decay rate fitting the positron excess as measured by PAMELA. This is the first time that DM filaments are shown to be promising targets for indirect detection of DM. On the other hand, the prospects for detectability of annihilating DM in local extragalactic structures are less optimistic even with extreme cross-sections. We make the DM density and density squared maps publicly available online.

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The ART of Cosmological Simulations

Cited by 4 publications

References 18 publications

The MultiDark Database: Release of the Bolshoi and MultiDark cosmological simulations

The MultiDark Database: Release of the Bolshoi and MultiDark cosmological simulations

Cosmological constraints from the large-scale weak lensing of SDSS MaxBCG clusters

DARK MATTER DECAY AND ANNIHILATION IN THE LOCAL UNIVERSE: CLUES FROM FERMI

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