A High-Fidelity Realization of the Euclid Code Comparison NN-body Simulation with Abacus

We present a high-fidelity realization of the cosmological $N$-body simulation from the Schneider et al. (2016) code comparison project. The simulation was performed with our Abacus $N$-body code, which offers high force accuracy, high performance, and minimal particle integration errors. The simulation consists of $2048^3$ particles in a $500\ h^{-1}\mathrm{Mpc}$ box, for a particle mass of $1.2\times 10^9\ h^{-1}\mathrm{M}_\odot$ with $10\ h^{-1}\mathrm{kpc}$spline softening. Abacus executed 1052 global time steps to$z=0$in 107 hours on one dual-Xeon, dual-GPU node, for a mean rate of 23 million particles per second per step. We find Abacus is in good agreement with Ramses and Pkdgrav3 and less so with Gadget3. We validate our choice of time step by halving the step size and find sub-percent differences in the power spectrum and 2PCF at nearly all measured scales, with$<0.3\%$errors at$k<10\ \mathrm{Mpc}^{-1}h$. On large scales, Abacus reproduces linear theory better than$0.01\%$. Simulation snapshots are available at http://nbody.rc.fas.harvard.edu/public/S2016 .Comment: 13 pages, 8 figures. Minor changes to match MNRAS accepted versio

The Abacus Cosmos: A Suite of Cosmological N-body Simulations

We present a public data release of halo catalogs from a suite of 125 cosmological $N$-body simulations from the Abacus project. The simulations span 40 $w$CDM cosmologies centered on the Planck 2015 cosmology at two mass resolutions, $4\times 10^{10}\;h^{-1}M_\odot$ and $1\times 10^{10}\;h^{-1}M_\odot$, in $1.1\;h^{-1}\mathrm{Gpc}$ and $720\;h^{-1}\mathrm{Mpc}$ boxes, respectively. The boxes are phase-matched to suppress sample variance and isolate cosmology dependence. Additional volume is available via 16 boxes of fixed cosmology and varied phase; a few boxes of single-parameter excursions from Planck 2015 are also provided. Catalogs spanning $z=1.5$ to $0.1$ are available for friends-of-friends and Rockstar halo finders and include particle subsamples. All data products are available at https://lgarrison.github.io/AbacusCosmosComment: 13 pages, 9 figures, 3 tables. Additional figures added for mass resolution convergence tests, and additional redshifts added for existing tests. Matches ApJS accepted versio

The halo light cone catalogues of AbacusSummit

We describe a method for generating halo catalogues on the light-cone using the ABACUSSUMMIT suite of N-body simulations. The main application of these catalogues is the construction of realistic mock galaxy catalogues and weak lensing maps on the sky. Our algorithm associates the haloes from a set of coarsely spaced snapshots with their positions at the time of light-cone crossing by matching halo particles to on-the-fly light-cone particles. It then records the halo and particle information into an easily accessible product, which we call the ABACUSSUMMIT halo light-cone catalogues. Our recommended use of this product is in the halo mass regime of Mhalo > 2.1 × 1011 M⊙ h−1 for the base resolution simulations, i.e. haloes containing at least 100 particles, where the interpolated halo properties are most reliable. To test the validity of the obtained catalogues, we perform various visual inspections and consistency checks. In particular, we construct galaxy mock catalogues of emission-line galaxies (ELGs) at z ∼ 1 by adopting a modified version of the ABACUSHOD script, which builds on the standard halo occupation distribution (HOD) method by including various extensions. We find that the multipoles of the autocorrelation function are consistent with the predictions from the full-box snapshot, implicitly validating our algorithm. In addition, we compute and output CMB convergence maps and find that the auto- and cross-power spectrum agrees with the theoretical prediction at the sub-per-cent level

Emulating galaxy clustering and galaxy–galaxy lensing into the deeply non-linear regime: methodology, information, and forecasts

The combination of galaxy-galaxy lensing (GGL) with galaxy clustering is one of the most promising routes to determining the amplitude of matter clustering at low redshifts. We show that extending clustering+GGL analyses from the linear regime down to similar to 0.5 h(-1) Mpc scales increases their constraining power considerably, even after marginalizing over a flexible model of non-linear galaxy bias. Using a grid of cosmological N-body simulations, we construct a Taylor-expansion emulator that predicts the galaxy autocorrelation xi(gg)(r) and galaxy-matter cross-correlation xi(gm) (r) as a function of sigma(8), Omega(m), and halo occupation distribution (HOD) parameters, which are allowed to vary with large-scale environment to represent possible effects of galaxy assembly bias. We present forecasts for a fiducial case that corresponds to BOSS LOWZ galaxy clustering and SDSS-depth weak lensing (effective source density similar to 0.3 arcmin(-2)). Using tangential shear and projected correlation function measurements over 0.5 2 h(-1) Mpc, 4 h(-1) Mpc for gamma(t) , omega(p)). Much of this improvement comes from the non-linear clustering information, which breaks degeneracies among HOD parameters. Increasing the effective source density to 3 arcmin(-2) sharpens the constraint on sigma(8)Omega(0.6 )(m)by a further factor of two. With robust modelling into the non-linear regime, low-redshift measurements of matter clustering at the 1-per cent level with clustering+GGL alone are well within reach of current data sets such as those provided by the Dark Energy Survey.National Science Foundation Graduate Research Fellowship Program [DGE-1343012]; Department of Energy Computational Science Graduate Fellowship Program of the Office of Science; National Nuclear Security Administration in the Department of Energy [DE-FG02-97ER25308]; National Science Foundation [AST-1516997, AST-1313285, 1228509]; Department of Energy Office of Science grant [DOE-SC0013718]; Simons Foundation Investigator; Center for Cosmology and AstroParticle Physics at the Ohio State University; Faculty of Arts and Sciences Division of Science, Research Computing Group at Harvard UniversityThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]