The Q Continuum Simulation: Harnessing the Power of Gpu Accelerated Supercomputers

Heitmann, Katrin; Frontiere, Nicholas; Sewell, Chris; Habib, Salman; Pope, Adrian; Finkel, Hal; Rizzi, Silvio; Insley, Joe; Bhattacharya, Suman

doi:10.1088/0067-0049/219/2/34

Cited by 55 publications

(50 citation statements)

References 86 publications

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“…However, the shape of f (σ ) found in simulations differs significantly from the analytic model originally proposed, agreeing much better with a version motivated by ellipsoidal rather than spherical collapse 81 . The detailed form of f (σ ) depends, among others, on simulation details and halo mass definitions, and a variety of empirical fitting functions have been published 77,[82][83][84][85] .…”

Section: Some Key Results Of N-body Simulationsmentioning

confidence: 99%

Cosmological simulations of galaxy formation

et al. 2020

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Over the last decades, cosmological simulations of galaxy formation have been instrumental for advancing our understanding of structure and galaxy formation in the Universe. These simulations follow the nonlinear evolution of galaxies modeling a variety of physical processes over an enormous range of scales. A better understanding of the physics relevant for shaping galaxies, improved numerical methods, and increased computing power have led to simulations that can reproduce a large number of observed galaxy properties. Modern simulations model dark matter, dark energy, and ordinary matter in an expanding space-time starting from well-defined initial conditions. The modeling of ordinary matter is most challenging due to the large array of physical processes affecting this matter component. Cosmological simulations have also proven useful to study alternative cosmological models and their impact on the galaxy population. This review presents a concise overview of the methodology of cosmological simulations of galaxy formation and their different applications. arXiv:1909.07976v4 [astro-ph.GA] 23 Oct 2019 become important for cosmological studies since they can, for example, explore the impact of alternative cosmological models on the galaxy population. Cosmological simulations of galaxy formation therefore provide important insights into a wide range of problems in astrophysics and cosmology. The most important components of cosmological galaxy formation simulations are discussed in this review. A schematic overview of the different ingredients of cosmological simulations is presented in Figure 2. Cosmological FrameworkCosmological simulations of galaxy formation are performed within a cosmological model and start from specific initial conditions. Both of these ingredients are now believed to be known to high precision. Cosmological ModelVarious observations revealed that our Universe is geometrically flat and dominated by dark matter and dark energy accounting for about ∼ 95% of the energy density. Standard model particles make up for the remaining ∼ 5% and are collectively referred to as baryons. The leading model for structure formation assumes that dark matter is cold, with negligible random motions when decoupled from other matter, and collisionless, so-called cold dark matter, and dark energy is represented by a cosmological constant Λ, which drives the accelerated expansion of the Universe. This leads to the concordance ΛCDM model, which builds the framework for galaxy formation. Measurements of the cosmic microwave background combined with other observations such as the distance-redshift relation from Type Ia supernovae, abundances of galaxy clusters, and galaxy clustering constrain the fundamental parameters of the ΛCDM model 3 . Initial ConditionsInitial conditions for cosmological simulations specify the perturbations imposed on top of a homogeneous expanding background. The background model is generally taken to be a spatially flat Friedmann-Lemaître-Robertson-Walker space-time with a defined compositio...

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Section: Some Key Results Of N-body Simulationsmentioning

confidence: 99%

Cosmological simulations of galaxy formation

et al. 2020

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“…While our results are highly encouraging, and suggest that a single N -body simulation can be recycled repeatedly, to produce as many as 10 4 independent shear power spectra or peak count histograms. In order to scale our results to large future surveys, such as LSST, it will be necessary to determine if our findings hold when challenged by larger and higher-resolution N -body simulations [45]. …”

Section: Discussionmentioning

confidence: 99%

Sample variance in weak lensing: How many simulations are required?

2016

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Constraining cosmology using weak gravitational lensing consists of comparing a measured feature vector of dimension N b with its simulated counterpart. An accurate estimate of the N b × N b feature covariance matrix C is essential to obtain accurate parameter confidence intervals. When C is measured from a set of simulations, an important question is how large this set should be. To answer this question, we construct different ensembles of Nr realizations of the shear field, using a common randomization procedure that recycles the outputs from a smaller number Ns ≤ Nr of independent ray-tracing N -body simulations. We study parameter confidence intervals as a function of (Ns, Nr) in the range 1 ≤ Ns ≤ 200 and 1 ≤ Nr 10 5 . Previous work [1] has shown that Gaussian noise in the feature vectors (from which the covariance is estimated) lead, at quadratic order, to an O(1/Nr) degradation of the parameter confidence intervals. Using a variety of lensing features measured in our simulations, including shear-shear power spectra and peak counts, we show that cubic and quartic covariance fluctuations lead to additional O(1/N 2 r ) error degradation that is not negligible when Nr is only a factor of few larger than N b . We study the large Nr limit, and find that a single, 240Mpc/h sized 512 3 -particle N -body simulation (Ns = 1) can be repeatedly recycled to produce as many as Nr = few × 10 4 shear maps whose power spectra and high-significance peak counts can be treated as statistically independent. As a result, a small number of simulations (Ns = 1 or 2) is sufficient to forecast parameter confidence intervals at percent accuracy.PACS numbers: 95.36.+x, 95.30.Sf, 98.62.Sb

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“…Based on large N -body simulations (Heitmann et al 2015;Habib et al 2016), Child et al (2018) give numerous fitting functions based on different functional forms and halo samples. We compare our model to their formulation as a function of M/M * for all individual halos (as opposed to stacked or relaxed halos).…”

Section: Comparison With Previous Modelsmentioning

confidence: 99%

An Accurate Physical Model for Halo Concentrations

Diemer¹,

Joyce

2019

ApJ

225

189

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The relation between halo mass, M , and concentration, c, is a critical component in our understanding of the structure of dark matter halos. While numerous models for this relation have been proposed, almost none of them attempt to derive the evolution of the relation analytically. We build on previous efforts to model the c-M relation as a function of physical parameters such as the peak height, ν, and the effective power spectrum slope, n eff , which capture the dependence of c on halo mass, redshift, and cosmology. We present three major improvements over previous models. First, we derive an analytical expression for the c-M relation that is valid under the assumption of pseudo-evolution, i.e., assuming that the density profiles of halos are static in physical coordinates while the definition of their boundary evolves. We find that this ansatz is highly successful in describing the evolution of the low-mass end of the c-M relation. Second, we employ a new physical variable, the effective exponent of linear growth, α eff , to parameterize deviations from an Einstein-de Sitter expansion history. Third, we combine an updated definition of n eff with the additional dependence on α eff and propose a phenomenological extension of our analytical framework to include all halo masses. This semianalytical model matches simulated concentrations in both scale-free models and ΛCDM to 5% accuracy with very few exceptions and differs significantly from all previously proposed models. We present a publicly available code to compute the predictions of our model in the python toolkit Colossus, including updated parameters for the model of Diemer and Kravtsov.

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The Q Continuum Simulation: Harnessing the Power of Gpu Accelerated Supercomputers

Cited by 55 publications

References 86 publications

Cosmological simulations of galaxy formation

Cosmological simulations of galaxy formation

Sample variance in weak lensing: How many simulations are required?

An Accurate Physical Model for Halo Concentrations

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