FastPM: a new scheme for fast simulations of dark matter and haloes

By Yu Feng, M. Chu, Uros Seljak, P. Mcdonald. 2016

168 citations recorded by the source corpus.

We introduce FastPM, a highly-scalable approximated particle mesh N-body solver, which implements the particle mesh (PM) scheme enforcing correct linear displacement (1LPT) evolution via modified kick and drift factors. Employing a 2-dimensional domain decomposing scheme, FastPM scales extremely well with a very large number of CPUs. In contrast to COmoving-LAgrangian (COLA) approach, we do not require to split the force or track separately the 2LPT solution, reducing the code complexity and memory requirements. We compare FastPM with different number of steps ($N_s$) and force resolution factor ($B$) against 3 benchmarks: halo mass function from Friends of Friends halo finder, halo and dark matter power spectrum, and cross correlation coefficient (or stochasticity), relative to a high resolution TreePM simulation. We show that the modified time stepping scheme reduces the halo stochasticity when compared to COLA with the same number of steps and force resolution. While increasing $N_s$ and $B$ improves the transfer function and cross correlation coefficient, for many applications FastPM achieves sufficient accuracy at low $N_s$ and $B$. For example, $N_s=10$ and $B=2$ simulation provides a substantial saving (a factor of 10) of computing time relative to $N_s=40$, $B=3$ simulation, yet the halo benchmarks are very similar at $z=0$. We find that for abundance matched halos the stochasticity remains low even for $N_s=5$. FastPM compares well against less expensive schemes, being only 7 (4) times more expensive than 2LPT initial condition generator for $N_s=10$ ($N_s=5$). Some of the applications where FastPM can be useful are generating a large number of mocks, producing non-linear statistics where one varies a large number of nuisance or cosmological parameters, or serving as part of an initial conditions solver.

Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics, Statistical and Nonlinear Physics

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Citations in the corpus

nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure (2017, methodology): “The rise of particle mesh simulation methods (Merz et al. 2005; Tassev et al. 2013; White et al. 2014; Feng et al. 2016) has offered a computationally cheaper alternative to running full N -body simulations.”
Exploring the posterior surface of the large scale structure reconstruction (2018, methodology): “The non-linear density is calculated with a 10 step FastPM scheme.”
The Quijote Simulations (2019, background): “He et al. (2019) showed that the highly nonlinear structure formation process, simulated with particle-mesh (PM) gravity solver (Feng et al. 2016) with fixed cosmological parameters, can be emulated with convolutional neural networks (CNNs).”
A volumetric deep Convolutional Neural Network for simulation of dark matter halo catalogues (2018, background): “…of large scale structure which attempt to create simulations of a satisfactory accuracy at minimal computational cost (Bond & Myers 1996a; Monaco et al. 2013; Tassev et al. 2013; Izard et al. 2016; Feng et al. 2016; Avila et al. 2015; Kitaura et al. 2014; Chuang et al. 2015; White et al. 2014).”