Convergence of halo statistics: code comparison between Rockstar and CompaSO using scale-free simulations

TL;DR

This paper compares halo-finder algorithms Rockstar and CompaSO using large scale-free simulations, demonstrating their convergence in mass functions and velocities at high precision, and analyzing differences at small scales.

Contribution

It provides a detailed code comparison of Rockstar and CompaSO, establishing convergence levels and analyzing the impact of post-processing on halo properties.

Findings

Mass functions converge at 1% precision

Mean pairwise velocities converge at 2% precision

Rockstar shows greater self-similarity at small scales

Abstract

In this study, we perform a halo-finder code comparison between Rockstar and CompaSO. Based on our previous analysis aiming at quantifying resolution of $N$-body simulations by exploiting large (up to $N=4096^3$) simulations of scale-free cosmologies run using Abacus, we focus on convergence of the HMF, 2PCF and mean radial pairwise velocities of halo centres selected with the aforementioned two algorithms. We establish convergence, for both Rockstar and CompaSO, of mass functions at the $1\%$ precision level and of the mean pairwise velocities (and also 2PCF) at the $2\%$ level. At small scales and small masses, we find that Rockstar exhibits greater self-similarity, and we also highlight the role played by the merger-tree post-processing of CompaSO halos on their convergence. Finally, we give resolution limits expressed as a minimum particle number per halo in a form that can be directly extrapolated to LCDM.