Multi-mass schemes for collisionless N-body simulations

TL;DR

This paper introduces a multi-mass N-body simulation scheme that optimizes particle distribution to reduce shot noise and two-body relaxation effects, improving the accuracy of collisionless galaxy models.

Contribution

The authors develop a general importance sampling method to create multi-mass realizations that enhance sampling in key regions, reducing simulation noise and relaxation effects.

Findings

Achieves ~100-fold reduction in acceleration field variance.

Reduces diffusion coefficients by similar factors in simulations.

Enhances accuracy of collisionless galaxy modeling.

Abstract

We present a general scheme for constructing Monte Carlo realizations of equilibrium, collisionless galaxy models with known distribution function (DF) f_0. Our method uses importance sampling to find the sampling DF f_s that minimizes the mean-square formal errors in a given set of projections of the DF f_0. The result is a multi-mass N-body realization of the galaxy model in which ``interesting'' regions of phase-space are densely populated by lots of low-mass particles, increasing the effective N there, and less interesting regions by fewer, higher-mass particles. As a simple application, we consider the case of minimizing the shot noise in estimates of the acceleration field for an N-body model of a spherical Hernquist model. Models constructed using our scheme easily yield a factor ~100 reduction in the variance in the central acceleration field when compared to a traditional equal-mass model with the same number of particles. When evolving both models with a real N-body code, the diffusion coefficients in our model are reduced by a similar factor. Therefore, for certain types of problems, our scheme is a practical method for reducing the two-body relaxation effects, thereby bringing the N-body simulations closer to the collisionless ideal.