MultiDark simulations: the story of dark matter halo concentrations and density profiles

By Anatoly Klypin, G. Yepes, S. Gottlober, Francisco Prada, S. Hess. 2014

689 citations recorded by the source corpus.

Accurately predicting structural properties of dark matter halos is one of the fundamental goals of modern cosmology. We use the new suite of MultiDark cosmological simulations to study the evolution of dark matter halo density profiles, concentrations, and velocity anisotropies. The MultiDark simulations cover a large range of masses 1e10-1e15Msun and volumes upto 50Gpc**3. The total number of dark matter halos in all the simulations exceeds 60 billion. We find that in order to understand the structure of dark matter halos and to make ~1% accurate predictions for density profiles, one needs to realize that halo concentration is more complex than the traditional ratio of the virial radius to the core radius in the NFW profile. For massive halos the averge density profile is far from the NFW shape and the concentration is defined by both the core radius and the shape parameter alpha in the Einasto approximation. Combining results from different redshifts, masses and cosmologies, we show that halos progress through three stages of evolution. (1) They start as rare density peaks that experience very fast and nearly radial infall. This radial infall brings mass closer to the center producing a high concentrated halo. Here, the halo concentration increases with the increasing halo mass and the concentration is defined by the alpha parameter with nearly constant core radius. Later halos slide into (2) the plateau regime where the accretion becomes less radial, but frequent mergers still affect even the central region. Now the concentration does not depend on halo mass. (3) Once the rate of accretion slows down, halos move into the domain of declining concentration-mass relation because new accretion piles up mass close to the virial radius while the core radius is staying constant. We provide accurate analytical fits to the numerical results for halo density profiles and concentrations.

Subfields: Astronomy and Astrophysics, Ecology, Instrumentation

View Paper: https://www.semanticscholar.org/paper/c89ae15e1e6bdac4266dfc4892b7131399aa47fb

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Citations in the corpus

The cosmological analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure (2019, methodology): “The catalogues have been calibrated to an N-body based reference sample extracted from one of the BigMultiDark simulations [77], which were performed using Gadget-2 [78] with 3840(3) particles in a volume of [2500h−1Mpc]3 assuming a ΛCDM cosmology with Ωm = 0.”
Lensing is Low: Cosmology, Galaxy Formation, or New Physics? (2016, methodology): “Their mock is created from a light-cone built from 20 outputs of the BigMDPL simulation (Klypin et al. 2016) and accounts for the geometry of the BOSS survey as well as for veto masks.”
The VIMOS Public Extragalactic Redshift Survey (VIPERS): The growth of structure at $0.5 < z < 1.2$ from redshift-space distortions in the clustering of the PDR-2 final sample (2016, methodology): “These have been constructed from the Big Mul-tiDark N-body simulation (Klypin et al. 2016), which assumes a ﬂat Λ CDM cosmology with ( Ω m , Ω Λ , Ω b , h , n s , σ 8 ) = (0 .”
Small-Scale Challenges to the ΛCDM Paradigm (2017, background): “…simulations have progressed to the point that we now have a fairly robust understanding of the structure of ΛCDM halos and the important factors that govern halo-to-halo variance (e.g., Navarro et al. 2010; Diemer & Kravtsov 2015; Klypin et al. 2016), at least for dark-matter-only simulations.”
Disruption of Dark Matter Substructure: Fact or Fiction? (2017, methodology): “For example, the Millennium simulation (Springel et al. 2005) has ε/l ∼ 0.022, while the MDPL and SMDPL simulations of Klypin et al. (2016) adopt ε/l ∼ 0.019 and ε/l ∼ 0.014, respectively.”
Halo and subhalo demographics with Planck cosmological parameters: Bolshoi–Planck and MultiDark–Planck simulations (2016, methodology): “For comparison, the green solid line shows the fitting function to the observed Local Volume velocity function reported in Klypin et al. (2016).”
COLOSSUS: A Python Toolkit for Cosmology, Large-scale Structure, and Dark Matter Halos (2017, background): “…c200c for any mass, redshift, or cosmology klypin16 m Klypin et al. 2016 Power-law fit, c200c and cvir for M > 1010 h−1 M , 0 < z < 5, planck13 or WMAP7 (function of M) klypin16 nu Klypin et al. 2016 Power-law fit, c200c and cvir for M > 1010 h−1 M , 0 < z < 5, planck13 (function of ν) ludlow16…”
Combining full-shape and BAO analyses of galaxy power spectra: a 1.6% CMB-independent constraint on H0 (2020, methodology): “Here, we use the MultiDark-Patchy (hereafter Patchy) [87, 88], which are generated with approximate Lagrangian perturbation theory techniques, with a stochastic halo bias prescription, calibrated from a high-resolution N-body simulation; BigMultiDark [89].”
The mass–concentration–redshift relation of cold and warm dark matter haloes (2016, methodology): “…al. 2007), or the mass-fraction in substructure (e.g. Neto et al. 2007; Ludlow et al. 2012); others, such as the virial ratio, η = 2 K/|U| (e.g. Cole & Lacey 1996; Bett et al. 2007; Knebe & Power 2008), or spin parameter, λ (e.g. Klypin et al. 2016), gauge the internal dynamical state of the halo.”
The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: single-probe measurements from CMASS anisotropic galaxy clustering (2013, methodology): “These mock catalogues were constructed using a similar procedure described in Rodriguez-Torres et al. 2016 where they constructed the BOSS DR12 lightcone mock catalogues using the MultiDark N-body simulations (Klypin et al. 2016).”
Proposal : Scalable , Active and Flexible Learning on Distributions (2015, neutral): “5 We assembled a catalog of massive halos from the MultiDark mdpl simulation (Klypin et al. 2014).The catalog contains 5 028 unique halos. Since we use only line-of-sight velocities, however, we can view each halo from multiple directions. For hyperparameter selection and testing, we use lines of si”
On the estimation of the local dark matter density using the rotation curve of the Milky Way (2019, result): “[46] A.”
The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: baryon acoustic oscillations in the Fourier space (2016, methodology): “The reference catalogue is extracted from one of the BIGMULTIDARK simulations (Klypin et al. 2016), which was performed using GADGET-2 (Springel 2005) with 38403 particles on a volume of (2.5 h−1 Mpc)3 assuming a CDM cosmology with M = 0.307115, b = 0.048206, σ 8 = 0.8288, ns = 0.9611 and a Hubble…”
The milky way total mass profile as inferred from Gaia DR2 (2019, background): “…Kafle et al. 2014; McMillan 2017; Monari et al. 2018; Lin & Li 2019), that is in tension with theoretical expectations based on cosmological simulations, which predict a mean concentration of ∼10 and a 68 percentile range of ∼[6, 10] (Ludlow et al. 2014; Hellwing et al. 2016; Klypin et al. 2016).”
EMERGE – an empirical model for the formation of galaxies since z ∼ 10 (2017, background): “The formation and evolution of dark matter haloes has been studied extensively with large cosmological N-body simulations (Springel et al. 2005; Boylan-Kolchin et al. 2009; Klypin, TrujilloGomez & Primack 2011; Angulo et al. 2012; Klypin et al. 2016).”
An Accurate Physical Model for Halo Concentrations (2018, methodology): “On the other hand, removing the upturn by aggressively cutting out unrelaxed halos necessarily leads to a bias toward dynamically older systems and thus higher concentrations (Ludlow et al. 2012; Prada et al. 2012; Correa et al. 2015c; Angel et al. 2016; Klypin et al. 2016; Child et al. 2018).”