arXiv subjects

Lehman H. Garrison

Publications and source records attributed to Lehman H. Garrison.

At least 37 records · Page 2Linked to original sources

arXiv2021

The $\texttt{Abacus}$ Cosmological $N$-body Code

We present $\texttt{Abacus}$, a fast and accurate cosmological $N$-body code based on a new method for calculating the gravitational potential from a static multipole mesh. The method analytically separates the near- and far-field forces, reducing the former to direct $1/r^2$ summation and the latter to a discrete convolution over multipoles. The method achieves 70 million particle updates per second per node of the Summit supercomputer, while maintaining a median fractional force error of $10^{-5}$. We express the simulation time step as an event-driven "pipeline", incorporating asynchronous events such as completion of co-processor work, Input/Output, and network communication. $\texttt{Abacus}$ has been used to produce the largest suite of $N$-body simulations to date, the $\texttt{AbacusSummit}$ suite of 60 trillion particles (Maksimova et al., 2021), incorporating on-the-fly halo finding. $\texttt{Abacus}$ enables the production of mock catalogs of the volume and resolution required by the coming generation of cosmological surveys.

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arXiv2021

AbacusSummit: A Massive Set of High-Accuracy, High-Resolution $N$-Body Simulations

We present the public data release of the AbacusSummit cosmological $N$-body simulation suite, produced with the $\texttt{Abacus}$ $N$-body code on the Summit supercomputer of the Oak Ridge Leadership Computing Facility. $\texttt{Abacus}$ achieves $\mathcal{O}\left(10^{-5}\right)$ median fractional force error at superlative speeds, calculating 70M particle updates per second per node at early times, and 45M particle updates per second per node at late times. The simulation suite totals roughly 60 trillion particles, the core of which is a set of 139 simulations with particle mass $2\times10^{9}\,h^{-1}\mathrm{M}_\odot$ in box size $2\,h^{-1}\mathrm{Gpc}$. The suite spans 97 cosmological models, including Planck 2018, previous flagship simulation cosmologies, and a linear derivative and cosmic emulator grid. A sub-suite of 1883 boxes of size $500\,h^{-1}\mathrm{Mpc}$ is available for covariance estimation. AbacusSummit data products span 33 epochs from $z=8$ to $0.1$ and include lightcones, full particle snapshots, halo catalogs, and particle subsets sampled consistently across redshift. AbacusSummit is the largest high-accuracy cosmological $N$-body data set produced to date.

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arXiv2021

\textsc{CompaSO}: A new halo finder for competitive assignment to spherical overdensities

We describe a new method (\textsc{CompaSO}) for identifying groups of particles in cosmological $N$-body simulations. \textsc{CompaSO} builds upon existing spherical overdensity (SO) algorithms by taking into consideration the tidal radius around a smaller halo before competitively assigning halo membership to the particles. In this way, the \textsc{CompaSO} finder allows for more effective deblending of haloes in close proximity as well as the formation of new haloes on the outskirts of larger ones. This halo-finding algorithm is used in the \textsc{AbacusSummit} suite of $N$-body simulations, designed to meet the cosmological simulation requirements of the Dark Energy Spectroscopic Instrument (DESI) survey. \textsc{CompaSO} is developed as a highly efficient on-the-fly group finder, which is crucial for enabling good load-balancing between the GPU and CPU and the creation of high-resolution merger trees. In this paper, we describe the halo-finding procedure and its particular implementation in \Abacus{Abacus}, accompanying it with a qualitative analysis of the finder. {We test the robustness of the \textsc{CompaSO} catalogues before and after applying the cleaning method described in an accompanying paper and demonstrate its effectiveness by comparing it with other validation techniques.} We then visualise the haloes and their density profiles, finding that they are well fit by the NFW formalism. Finally, we compare other properties such as radius-mass relationships and two-point correlation functions with that of another widely used halo finder, \textsc{ROCKSTAR}.

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arXiv2021

The halo light cone catalogues of \Abacus{AbacusSummit}

We describe a method for generating halo catalogues on the light cone using the \Abacus{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 \Abacus{AbacusSummit} halo light cone catalogues. Our recommended use of this product is in the halo mass regime of $M_{\rm halo} > 2.1 \times 10^{11} \ M_\odot/h$ for the \texttt{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 $z \sim 1$ by adopting a modified version of the \Abacus{AbacusHOD} script, which builds on the standard halo occupation distribution (HOD) method by including various extensions. We find that the multipoles of the auto-correlation 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 subpercent level. Halo light cone catalogues for 25 \texttt{base} and 2 \texttt{huge} simulations at the fiducial cosmology is available at DOI:\href{https://www.doi.org/10.13139/OLCF/1825069}{10.13139/OLCF/1825069}

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arXiv2021

Self-Similarity of $k$-Nearest Neighbor Distributions in Scale-Free Simulations

We use the $k$-nearest neighbor probability distribution function ($k$NN-PDF, Banerjee & Abel 2021) to assess convergence in a scale-free $N$-body simulation. Compared to our previous two-point analysis, the $k$NN-PDF allows us to quantify our results in the language of halos and numbers of particles, while also incorporating non-Gaussian information. We find good convergence for 32 particles and greater at densities typical of halos, while 16 particles and fewer appears unconverged. Halving the softening length extends convergence to higher densities, but not to fewer particles. Our analysis is less sensitive to voids, but we analyze a limited range of underdensities and find evidence for convergence at 16 particles and greater even in sparse voids.

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arXiv2021

Good and Proper: Self-similarity of N-body Simulations with Proper Force Softening

Analysis of self-similarity in scale-free $N$-body simulations reveals the spatial and temporal scales for which statistics measured in cosmological simulations are converged to the physical continuum limit. We examine how the range of scales in which the two-point correlation function is converged depends on the force softening length and whether it is held constant in comoving or proper coordinates. We find that a proper softening that reaches roughly 1/30th of the inter-particle spacing by the end of the simulation resolves the same spatial and temporal scales as a comoving softening of the same length while using a third fewer time steps, for a range of scale factors typical to $Λ$CDM simulations. We additionally infer an inherent resolution limit, set by the particle mass and scaling as $a^{-1/2}$, beyond which reducing the softening does not improve the resolution. We postulate a mapping of these results with spectral index $n=-2$ to $Λ$CDM simulations.

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arXiv2021

Checkpointing with cp: the POSIX Shared Memory System

We present the checkpointing scheme of Abacus, an $N$-body simulation code that allocates all persistent state in POSIX shared memory, or ramdisk. Checkpointing becomes as simple as copying files from ramdisk to external storage. The main simulation executable is invoked once per time step, memory mapping the input state, computing the output state directly into ramdisk, and unmapping the input state. The main executable remains unaware of the concept of checkpointing, with the top-level driver code launching a file-system copy between executable invocations when a checkpoint is needed. Since the only information flow is through files on ramdisk, the checkpoint must be correct so long as the simulation is correct. However, we find that with multi-GB of state, there is a significant overhead to unmapping the shared memory. This can be partially mitigated with multithreading, but ultimately, we do not recommend shared memory for use with a large state.

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arXiv2020

Measured Lightcurves and Rotational Periods of 3122 Florence, 3830 Trelleborg, and (131077) 2000 YH105

We determined the rotational periods of 3122 Florence, 3830 Trelleborg, and (131077) 2000 YH105 with the Harvard Clay Telescope and KeplerCam at the Fred L. Whipple Observatory. We found the rotational periods to be 2.3580 $\pm$ 0.0015 h, 17.059 $\pm$ 0.017 h, and 1.813 $\pm$ 0.00003 h, respectively. Our measurement of 3122 Florence's period agrees with Warner (2016), who reported 2.3580 $\pm$ 0.0002 h.

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arXiv2019

Cosmology with galaxy-galaxy lensing on non-perturbative scales: Emulation method and application to BOSS LOWZ

We describe our nonlinear emulation (i.e., interpolation) framework that combines the halo occupation distribution (HOD) galaxy bias model with $N$-body simulations of nonlinear structure formation, designed to accurately predict the projected clustering and galaxy-galaxy lensing signals from luminous red galaxies (LRGs) in the redshift range $0.16 < z < 0.36$ on comoving scales $0.6 < r_p < 30$ \hMpc. The interpolation accuracy is $\lesssim 1-2$ per cent across the entire physically plausible range of parameters for all scales considered. We correctly recover the true value of the cosmological parameter $S_8 = ({σ_8}/{0.8228}) ({Ω_{\text{m}}}/{0.3107})^{0.6}$ from mock measurements produced via subhalo abundance matching (SHAM)-based lightcones designed to approximately match the properties of the SDSS LOWZ galaxy sample. Applying our model to Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 14 (DR14) LOWZ galaxy clustering and galaxy-shear cross-correlation measurements made with Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8) imaging, we perform a prototype cosmological analysis marginalizing over $w$CDM cosmological parameters and galaxy HOD parameters. We obtain a 4.4 per cent measurement of $S_8 = 0.847 \pm 0.037$, in $3.5σ$ tension with the Planck cosmological results of $1.00 \pm 0.02$. We discuss the possibility of underestimated systematic uncertainties or astrophysical effects that could explain this discrepancy.

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arXiv2019

Corrfunc: Blazing fast correlation functions with AVX512F SIMD Intrinsics

Correlation functions are widely used in extra-galactic astrophysics to extract insights into how galaxies occupy dark matter halos and in cosmology to place stringent constraints on cosmological parameters. A correlation function fundamentally requires computing pair-wise separations between two sets of points and then computing a histogram of the separations. Corrfunc is an existing open-source, high-performance software package for efficiently computing a multitude of correlation functions. In this paper, we will discuss the SIMD AVX512F kernels within Corrfunc, capable of processing 16 floats or 8 doubles at a time. The latest manually implemented Corrfunc AVX512F kernels show a speedup of up to $\sim 4\times$ relative to compiler-generated code for double-precision calculations. The AVX512F kernels show $\sim 1.6\times$ speedup relative to the AVX kernels and compare favorably to a theoretical maximum of $2\times$. In addition, by pruning pairs with too large of a minimum possible separation, we achieve a $\sim 5-10\%$ speedup across all the SIMD kernels. Such speedups highlight the importance of programming explicitly with SIMD vector intrinsics for complex calculations that can not be efficiently vectorized by compilers. Corrfunc is publicly available at https://github.com/manodeep/Corrfunc/.

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arXiv2019

Corrfunc --- A Suite of Blazing Fast Correlation Functions on the CPU

The two-point correlation function (2PCF) is the most widely used tool for quantifying the spatial distribution of galaxies. Since the distribution of galaxies is determined by galaxy formation physics as well as the underlying cosmology, fitting an observed correlation function yields valuable insights into both. The calculation for a 2PCF involves computing pair-wise separations and consequently, the computing time scales quadratically with the number of galaxies. The next-generation galaxy surveys are slated to observe many millions of galaxies, and computing the 2PCF for such surveys would be prohibitively time-consuming. Additionally, modern modelling techniques require the 2PCF to be calculated thousands of times on simulated galaxy catalogues of {\em at least} equal size to the data and would be completely unfeasible for the next generation surveys. Thus, calculating the 2PCF forms a substantial bottleneck in improving our understanding of the fundamental physics of the universe, and we need high-performance software to compute the correlation function. In this paper, we present Corrfunc --- a suite of highly optimised, OpenMP parallel clustering codes. The improved performance of Corrfunc arises from both efficient algorithms as well as software design that suits the underlying hardware of modern CPUs. Corrfunc can compute a wide range of 2-D and 3-D correlation functions in either simulation (Cartesian) space or on-sky coordinates. Corrfunc runs efficiently in both single- and multi-threaded modes and can compute a typical 2-point projected correlation function ($w_p(r_p)$) for ~1 million galaxies within a few seconds on a single thread. Corrfunc is designed to be both user-friendly and fast and is publicly available at https://github.com/manodeep/Corrfunc.

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arXiv2019

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

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 ($\sim0.07h^{-3}\,\mathrm{Gpc}^3$) 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 $\sim3\%$ and $\sim4\%$, respectively.

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arXiv2019

KELT-24b: A 5M$_{\rm J}$ Planet on a 5.6 day Well-Aligned Orbit around the Young V=8.3 F-star HD 93148

We present the discovery of KELT-24 b, a massive hot Jupiter orbiting a bright (V=8.3 mag, K=7.2 mag) young F-star with a period of 5.6 days. The host star, KELT-24 (HD 93148), has a $T_{\rm eff}$ =$6509^{+50}_{-49}$ K, a mass of $M_{*}$ = $1.460^{+0.055}_{-0.059}$ $M_{\odot}$, radius of $R_{*}$ = $1.506\pm0.022$ $R_{\odot}$, and an age of $0.78^{+0.61}_{-0.42}$ Gyr. Its planetary companion (KELT-24 b) has a radius of $R_{\rm P}$ = $1.272\pm0.021$ $R_{\rm J}$, a mass of $M_{\rm P}$ = $5.18^{+0.21}_{-0.22}$ $M_{\rm J}$, and from Doppler tomographic observations, we find that the planet's orbit is well-aligned to its host star's projected spin axis ($λ$ = $2.6^{+5.1}_{-3.6}$). The young age estimated for KELT-24 suggests that it only recently started to evolve from the zero-age main sequence. KELT-24 is the brightest star known to host a transiting giant planet with a period between 5 and 10 days. Although the circularization timescale is much longer than the age of the system, we do not detect a large eccentricity or significant misalignment that is expected from dynamical migration. The brightness of its host star and its moderate surface gravity make KELT-24b an intriguing target for detailed atmospheric characterization through spectroscopic emission measurements since it would bridge the current literature results that have primarily focused on lower mass hot Jupiters and a few brown dwarfs.

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arXiv2019

A High-Fidelity Realization of the Euclid Code Comparison $N$-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 .

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arXiv2019

Generating Approximate Halo Catalogs for Blind Challenges in Precision Cosmology

We present a method for generating suites of dark-matter halo catalogs with only a few $N$-body simulations, focusing on making small changes to the underlying cosmology of a simulation with high precision. In the context of blind challenges, this allows us to reuse a simulation by giving it a new cosmology after the original cosmology is revealed. Starting with full $N$-body realizations of an original cosmology and a target cosmology, we fit a transfer function that displaces halos in the original so that the galaxy/HOD power spectrum matches that of the target cosmology. This measured transfer function can then be applied to a new realization of the original cosmology to create a new realization of the target cosmology. For a 1% change in $σ_8$, we achieve 0.1% accuracy to $k = 1h\,\mathrm{Mpc}^{-1}$ in the real-space power spectrum; this degrades to 0.3% when the transfer function is applied to a new realization. We achieve similar accuracy in the redshift-space monopole and quadrupole. In all cases, the result is better than the sample variance of our $1.1h^{-1}\,\mathrm{Gpc}$ simulation boxes.

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arXiv2018

Exploring the squeezed three-point galaxy correlation function with generalized halo occupation distribution models

We present the GeneRalized ANd Differentiable Halo Occupation Distribution (GRAND-HOD) routine that generalizes the standard 5 parameter halo occupation distribution model (HOD) with various halo-scale physics and assembly bias. We describe the methodology of 4 different generalizations: satellite distribution generalization, velocity bias, closest approach distance generalization, and assembly bias. We showcase the signatures of these generalizations in the 2-point correlation function (2PCF) and the squeezed 3-point correlation function (squeezed 3PCF). We identify generalized HOD prescriptions that are nearly degenerate in the projected 2PCF and demonstrate that these degeneracies are broken in the redshift-space anisotropic 2PCF and the squeezed 3PCF. We also discuss the possibility of identifying degeneracies in the anisotropic 2PCF and further demonstrate the extra constraining power of the squeezed 3PCF on galaxy-halo connection models. We find that within our current HOD framework, the anisotropic 2PCF can predict the squeezed 3PCF better than its statistical error. This implies that a discordant squeezed 3PCF measurement could falsify the particular HOD model space. Alternatively, it is possible that further generalizations of the HOD model would open opportunities for the squeezed 3PCF to provide novel parameter measurements. The GRAND-HOD Python package is publicly available at https://github.com/SandyYuan/GRAND-HOD .

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arXiv2018

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/AbacusCosmos

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arXiv2017

Testing the Detection Significance on the Large Scale Structure by a JWST Deep Field Survey

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 $10^9$ to $10^{11}$ $h^{-1}\;M_\odot$, which are those expected to host galaxies detectable with JWST, are highly clustered with bias factors ranging from 5 and 30 depending strongly on mass, as well as on redshift and scale. This results in correlation lengths of 5--10$h^{-1}\;{\rm Mpc}$, similar to that of today's galaxies. Our results are based on a simulation of 130 billion particles in a box of $250h^{-1}\;{\rm Mpc}$ size using our new high-accuracy ABACUS simulation code, the corrections to cosmological initial conditions of (Garrison et al. 2016, 2016MNRAS.461.4125G), and the Planck 2015 cosmology. We use variations between sub-volumes to estimate the detectability of the clustering. Because of the very strong inter-halo clustering, we find that surveys of order 25$h^{-1}\;{\rm Mpc}$ comoving transverse size 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.

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