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

Garrison, Lehman H.; Eisenstein, Daniel J.; Ferrer, Douglas; Tinker, Jeremy L.; Pinto, Philip A.; Weinberg, David H.

doi:10.3847/1538-4365/aabfd3

Cited by 123 publications

(107 citation statements)

References 32 publications

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“…We use the publicly released data products produced as part of the AbacusCosmos N-body simulation suite 2 (Garrison et al 2018a) to test the statistical robustness of our results, in particular cosmic variance and box-size effects. We work with the halo catalog for a box of size 720 Mpc/h with 1440 3 dark matter particles which uses Planck 2015 cosmology (Garrison et al 2018b). Abacus delivers both high speed and accuracy, as it utilizes novel computational techniques and high performance hardware -e.g.…”

Section: Abacusmentioning

confidence: 99%

Limitations to the ‘basic’ HOD model and beyond

Hadzhiyska

Bose

Eisenstein

et al. 2020

Monthly Notices of the Royal Astronomical Society

112

118

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We make use of the IllustrisTNG cosmological, hydrodynamical simulations to test fundamental assumptions of the mass-based Halo Occupation Distribution (HOD) approach to modelling the galaxy-halo connection. By comparing the clustering of galaxies measured in the 300 Mpc TNG box (TNG300) with that predicted by the standard ("basic") HOD model (bHOD), we find that, on average, the bHOD model underpredicts the real-space correlation function in the TNG300 box by ∼ 15% on scales of 1 Mpc/h < r < 20 Mpc/h, which is well beyond the target precision demanded of next-generation galaxy redshift surveys. We perform several tests to establish the robustness of our findings to systematic effects, including the effect of finite box size and the choice of halo finder. In our exploration of "secondary" parameters with which to augment the bHOD, we find that the local environment of the halo, the velocity dispersion anisotropy, β, and the product of the half-mass radius and the velocity dispersion, σ 2 R halfmass , are the three most effective measures of assembly bias that help reconcile the bHOD-predicted clustering with that in TNG300. In addition, we test other halo properties such as halo spin, formation epoch and halo concentration. We also find that at fixed halo mass, galaxies in one type of environment cluster differently from galaxies in another. We demonstrate that a more complete model of the galaxy-halo connection can be constructed if both the mass and information regarding the local environment in which a halo is embedded are combined.

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Section: Abacusmentioning

confidence: 99%

Limitations to the ‘basic’ HOD model and beyond

Hadzhiyska

Bose

Eisenstein

et al. 2020

Monthly Notices of the Royal Astronomical Society

112

118

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“…For this purpose we use the halo occupation distribution (HOD; Berlind & Weinberg 2002) and marginalize over HOD parameters when constraining cosmological parameters as advocated by Zheng & Weinberg (2007). We follow the approach of Wibking et al (2019) to obtain accurate predictions in the non-linear regime by populating N-body halos from the AbacusCosmos suite of cosmological simulations (Metchnik 2009;Garrison et al 2018). Like Wibking et al (2019), we include an extended HOD parameter that allows the halo occupation to vary with large scale environment, to represent the possible effects of galaxy assembly bias (Hearin et al 2016;Zentner et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Cosmology with stacked cluster weak lensing and cluster–galaxy cross-correlations

Salcedo

Wibking

Weinberg

et al. 2019

Monthly Notices of the Royal Astronomical Society

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Cluster weak lensing is a sensitive probe of cosmology, particularly the amplitude of matter clustering σ 8 and matter density parameter Ω m . The main nuisance parameter in a cluster weak lensing cosmological analysis is the scatter between the true halo mass and the relevant cluster observable, denoted σ ln Mc . We show that combining the cluster weak lensing observable ∆Σ with the projected cluster-galaxy cross-correlation function w p,cg and galaxy auto-correlation function w p,gg can break the degeneracy between σ 8 and σ ln Mc to achieve tight, percent-level constraints on σ 8 . Using a grid of cosmological N-body simulations, we compute derivatives of ∆Σ, w p,cg , and w p,gg with respect to σ 8 , Ω m , σ ln Mc and halo occupation distribution (HOD) parameters describing the galaxy population. We also compute covariance matrices motivated by the properties of the Dark Energy Suvery (DES) cluster and weak lensing survey and the BOSS CMASS galaxy redshift survey. For our fiducial scenario combining ∆Σ, w p,cg , and w p,gg measured over 0.3 − 30.0 h −1 Mpc, for clusters at z = 0.35 − 0.55 above a mass threshold M c ≈ 2 × 10 14 h −1 M , we forecast a 1.4% constraint on σ 8 while marginalizing over σ ln Mc and all HOD parameters. Reducing the mass threshold to 1 × 10 14 h −1 M and adding a z = 0.15 − 0.35 redshift bin sharpens this constraint to 0.8%. The small scale (r p < 3.0 h −1 Mpc) "mass function" and large scale (r p > 3.0 h −1 Mpc) "halo-mass cross-correlation" regimes of ∆Σ have comparable constraining power, allowing internal consistency tests from such an analysis.

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“…1 https://lgarrison.github.io/AbacusCosmos/ See Garrison et al (2018) for more details about the AbacusCosmos suite of simulations.…”

Section: Abacuscosmos Simulationsmentioning

confidence: 99%

A Hybrid Deep Learning Approach to Cosmological Constraints from Galaxy Redshift Surveys

Ntampaka

Eisenstein

Yuan

et al. 2020

ApJ

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We present a deep machine learning (ML)-based technique for accurately determining σ 8 and Ω m from mock 3D galaxy surveys. The mock surveys are built from the AbacusCosmos suite of N -body simulations, which comprises 40 cosmological volume simulations spanning a range of cosmological models, and we account for uncertainties in galaxy formation scenarios through the use of generalized halo occupation distributions (HODs). We explore a trio of ML models: a 3D convolutional neural network (CNN), a power-spectrum-based fully connected network, and a hybrid approach that merges the two to combine physically motivated summary statistics with flexible CNNs. We describe best practices for training a deep model on a suite of matched-phase simulations and we test our model on a completely independent sample that uses previously unseen initial conditions, cosmological parameters, and HOD parameters. Despite the fact that the mock observations are quite small (∼ 0.07h −3 Gpc 3 ) and the training data span a large parameter space (6 cosmological and 6 HOD parameters), the CNN and hybrid CNN can constrain σ 8 and Ω m to ∼ 3% and ∼ 4%, respectively.

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

Cited by 123 publications

References 32 publications

Limitations to the ‘basic’ HOD model and beyond

Limitations to the ‘basic’ HOD model and beyond

Cosmology with stacked cluster weak lensing and cluster–galaxy cross-correlations

A Hybrid Deep Learning Approach to Cosmological Constraints from Galaxy Redshift Surveys

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