We train graph neural networks on halo catalogues from Gadget N-body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogues contain $\lesssim$5,000 halos with masses $\gtrsim 10^{10}~h^{-1}M_\odot$ in a periodic volume of $(25~h^{-1}{\rm Mpc})^3$; every halo in the catalogue is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of $\Omega_{\rm m}$ and $\sigma_8$ with a mean relative error of $\sim6\%$, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of $\Omega_{\rm m}$ and $\sigma_8$ when tested using halo catalogues from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP$^3$M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer $\Omega_{\rm m}$ also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N-body codes are not converged on the relevant scales corresponding to these parameters.
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/AbacusCosmos
Abstract The Dark Energy Spectroscopic Instrument (DESI) completed its 5 month Survey Validation in 2021 May. Spectra of stellar and extragalactic targets from Survey Validation constitute the first major data sample from the DESI survey. This paper describes the public release of those spectra, the catalogs of derived properties, and the intermediate data products. In total, the public release includes good-quality spectral information from 466,447 objects targeted as part of the Milky Way Survey, 428,758 as part of the Bright Galaxy Survey, 227,318 as part of the Luminous Red Galaxy sample, 437,664 as part of the Emission Line Galaxy sample, and 76,079 as part of the Quasar sample. In addition, the release includes spectral information from 137,148 objects that expand the scope beyond the primary samples as part of a series of secondary programs. Here, we describe the spectral data, data quality, data products, Large-Scale Structure science catalogs, access to the data, and references that provide relevant background to using these spectra.
Abstract The Dark Energy Spectroscopic Instrument (DESI) Survey has obtained a set of spectroscopic measurements of galaxies to validate the final survey design and target selections. To assist in these tasks, we visually inspect DESI spectra of approximately 2500 bright galaxies, 3500 luminous red galaxies (LRGs), and 10,000 emission-line galaxies (ELGs) to obtain robust redshift identifications. We then utilize the visually inspected redshift information to characterize the performance of the DESI operation. Based on the visual inspection (VI) catalogs, our results show that the final survey design yields samples of bright galaxies, LRGs, and ELGs with purity greater than 99%. Moreover, we demonstrate that the precision of the redshift measurements is approximately 10 km s −1 for bright galaxies and ELGs and approximately 40 km s −1 for LRGs. The average redshift accuracy is within 10 km s −1 for the three types of galaxies. The VI process also helps improve the quality of the DESI data by identifying spurious spectral features introduced by the pipeline. Finally, we show examples of unexpected real astronomical objects, such as Ly α emitters and strong lensing candidates, identified by VI. These results demonstrate the importance and utility of visually inspecting data from incoming and upcoming surveys, especially during their early operation phases.
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 .
Abstract In preparation for deep extragalactic imaging with the James Webb Space Telescope , we explore the clustering of massive halos at z = 8 and 10 using a large N -body simulation. We find that halos with masses of 10 9 –10 11 h −1 M ⊙ , which are those expected to host galaxies detectable with JWST , are highly clustered with bias factors ranging from 5 to 30 depending strongly on mass, as well as on redshift and scale. This results in correlation lengths of 5–10 h −1 Mpc, similar to those of today’s galaxies. Our results are based on a simulation of 130 billion particles in a box of size 250 h −1 Mpc using our new high-accuracy Abacus simulation code, the corrections to cosmological initial conditions of Garrison et al., and the Planck 2015 cosmology. We use variations between sub-volumes to estimate the detectability of the clustering. Because of the very strong interhalo clustering, we find that a medium-sized survey with a transverse size of the order of 25 h −1 comoving Mpc (about 13′) may be able to detect the clustering of z = 8–10 galaxies with only 500–1000 survey objects if the galaxies indeed occupy the most massive dark matter halos.
ABSTRACT We introduce the AbacusHOD model and present two applications of AbacusHOD and the AbacusSummit simulations to observations. AbacusHOD is a Halo Occupation Distribution (HOD) framework written in Python that is particle-based, multitracer, highly generalized, and highly efficient. It is designed specifically with multitracer/cosmology analyses for next-generation large-scale structure surveys in mind, and takes advantage of the volume and precision offered by the new state-of-the-art AbacusSummit cosmological simulations. The model is also highly customizable and should be broadly applicable to any upcoming surveys and a diverse range of cosmological analyses. In this paper, we demonstrate the capabilities of the AbacusHOD framework through two example applications. The first example demonstrates the high efficiency and the large HOD extension feature set through an analysis of full-shape redshift-space clustering of BOSS galaxies at intermediate to small scales ($\lt 30\, h^{-1}$ Mpc), assessing the necessity of introducing secondary galaxy biases (assembly bias). We find strong evidence for using halo environment instead of concentration to trace secondary galaxy bias, a result which also leads to a moderate reduction in the ‘lensing is low’ tension. The second example demonstrates the multitracer capabilities of the AbacusHOD package through an analysis of the extended Baryon Oscillation Spectroscopic Survey cross-correlation measurements between three different galaxy tracers: luminous red galaxies, emission-line galaxies, and quasi-stellar objects. We expect the AbacusHOD framework, in combination with the AbacusSummit simulation suite, to play an important role in a simulation-based analysis of the upcoming Dark Energy Spectroscopic Instrument data sets.
We present cosmological results from the measurement of clustering of galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). We adopt the full-shape (FS) modeling of the power spectrum, including the effects of redshift-space distortions, in an analysis which has been validated in a series of supporting papers. In the flat $\Lambda$CDM cosmological model, DESI (FS+BAO), combined with a baryon density prior from Big Bang Nucleosynthesis and a weak prior on the scalar spectral index, determines matter density to $\Omega_\mathrm{m}=0.2962\pm 0.0095$, and the amplitude of mass fluctuations to $\sigma_8=0.842\pm 0.034$. The addition of the cosmic microwave background (CMB) data tightens these constraints to $\Omega_\mathrm{m}=0.3056\pm 0.0049$ and $\sigma_8=0.8121\pm 0.0053$, while further addition of the the joint clustering and lensing analysis from the Dark Energy Survey Year-3 (DESY3) data leads to a 0.4% determination of the Hubble constant, $H_0 = (68.40\pm 0.27)\,{\rm km\,s^{-1}\,Mpc^{-1}}$. In models with a time-varying dark energy equation of state, combinations of DESI (FS+BAO) with CMB and type Ia supernovae continue to show the preference, previously found in the DESI DR1 BAO analysis, for $w_0>-1$ and $w_a<0$ with similar levels of significance. DESI data, in combination with the CMB, impose the upper limits on the sum of the neutrino masses of $\sum m_\nu < 0.071\,{\rm eV}$ at 95% confidence. DESI data alone measure the modified-gravity parameter that controls the clustering of massive particles, $\mu_0=0.11^{+0.45}_{-0.54}$, while the combination of DESI with the CMB and the clustering and lensing analysis from DESY3 constrains both modified-gravity parameters, giving $\mu_0 = 0.04\pm 0.22$ and $\Sigma_0 = 0.044\pm 0.047$, in agreement with general relativity. [Abridged.]
