Improving the Accuracy of Halo Mass Based Statistics For Fast Approximate N-body Simulations

TL;DR

This paper introduces an empirical correction method for fast N-body simulations that significantly improves the accuracy of halo mass-based statistics, matching full N-body results without extra computational cost.

Contribution

The authors develop a simple, parameter-adjustment scheme to correct halo properties in fast simulations, enhancing their precision to percent-level accuracy across various cosmologies.

Findings

Corrected halo catalogues match full N-body statistics within 1% accuracy.

Method improves halo mass-based statistics from redshift 0 to 1.

Calibration is robust across different cosmological models.

Abstract

Approximate N-body methods, such as FastPM and COLA, have been successful in modelling halo and galaxy clustering statistics, but their low resolution on small scales is a limitation for applications that require high precision. Full N-body simulations can provide better accuracy but are too computationally expensive for a quick exploration of cosmological parameters. This paper presents a method for correcting distinct haloes identified in fast N-body simulations, so that various halo statistics improve to a percent level accuracy. The scheme seeks to find empirical corrections to halo properties such that the virial mass is the same as that of a corresponding halo in a full N-body simulation. The modified outer density contour of the corrected halo is determined on the basis of the FastPM settings and the number of particles inside the halo. This method only changes some parameters of the halo finder, and does not require any extra CPU-cost. We demonstrate that the adjusted halo catalogues of FastPM simulations significantly improve the precision of halo mass-based statistics from redshifts $z=0.0$ to $1.0$, and that our calibration can be applied to different cosmologies without needing to be recalibrated.