The Convergence of Particle-in-Cell Schemes for Cosmological Dark Matter Simulations
Andrew Myers, Phillip Colella, Brian Van Straalen

TL;DR
This paper demonstrates that Particle-in-Cell schemes can reliably simulate dark matter evolution in cosmology by introducing regularization and remapping techniques, achieving convergence and avoiding unphysical clumping.
Contribution
The authors develop a modified PIC method with regularization and adaptive remapping, enabling second-order convergence in cosmological dark matter simulations.
Findings
PIC converges at second order on Zel'dovich Pancake problems.
Regularization reduces errors in particle trajectories.
Remapping prevents unphysical small-scale clumping.
Abstract
Particle methods are a ubiquitous tool for solving the Vlasov-Poisson equation in comoving coordinates, which is used to model the gravitational evolution of dark matter in an expanding universe. However, these methods are known to produce poor results on idealized test problems, particularly at late times, after the particle trajectories have crossed. To investigate this, we have performed a series of one- and two-dimensional "Zel'dovich Pancake" calculations using the popular Particle-in-Cell (PIC) method. We find that PIC can indeed converge on these problems provided the following modifications are made. The first modification is to regularize the singular initial distribution function by introducing a small but finite artificial velocity dispersion. This process is analogous to artificial viscosity in compressible gas dynamics, and, as with artificial viscosity, the amount of…
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