An Accurate P$^{3}$M Algorithm for Gravitational Lensing Studies in Simulations

Abstract

We present a two-dimensional (2D) Particle-Particle-Particle-Mesh (P$^3$M) algorithm with an optimized Green function and adaptive softening length for gravitational lensing studies in N-Body simulations. The analytical form of the optimized Green function $\hat{G}(\rm{k})$ is given. The softening schemes ($S$) are studied for both the PM and the PP calculations in order for accurate force calculation and suppression of the particle discreteness effect. Our method is two orders of magnitude more accurate than the simple PM algorithm with the {\it poor man's} Green function ($\propto1/k^2$) at a scale of a few mesh cells or smaller. The force anisotropy is also much smaller than the conventional PM calculation. We can achieve a force accuracy better than 0.1 percent at all scales with our algorithm, which makes it an ideal (accurate and fast) algorithm for {\textit{micro}} lensing studies . When we apply the algorithm to computing {\textit{weak}} and {\textit{strong}} lensing quantities in N-Body simulations, the errors are dominated by the Poisson noise caused by particle discreteness. The Poisson noise can be suppressed by smoothing out the particle distribution, which can be achieved by simply choosing an adaptive softening length in the PP calculation. We have presented a criterion to set the adaptive softening length. Our algorithm is also applicable to cosmological simulations. We provide a \textsc{python} implementation \texttt{P3Mlens} for this algorithm.