Rapid Simulations of Halo and Subhalo Clustering

TL;DR

This paper introduces a fast, approximate method for simulating halo and subhalo clustering using Lagrangian perturbation theory, enabling efficient galaxy survey analysis with good accuracy.

Contribution

The authors develop a new fast simulation technique for halo and subhalo clustering based on PINOCCHIO, including a novel subhalo merger time fitting function and spatial distribution modeling.

Findings

Simulations match N-body results in velocity and correlation functions.

The method is significantly faster than full N-body simulations.

Subhalo distribution modeling impacts clustering results.

Abstract

The analysis of cosmological galaxy surveys requires realistic simulations for their interpretation. Forward modelling is a powerful method to simulate galaxy clustering without the need for an underlying complex model. This approach requires fast cosmological simulations with a high resolution and large volume, to resolve small dark matter halos associated to single galaxies. In this work, we present fast halo and subhalo clustering simulations based on the Lagrangian perturbation theory code PINOCCHIO, which generates halos and merger trees. The subhalo progenitors are extracted from the merger history and the survival of subhalos is modelled. We introduce a new fitting function for the subhalo merger time, which includes a redshift dependence of the fitting parameters. The spatial distribution of subhalos within their hosts is modelled using a number density profile. We compare our simulations with the halo finder ROCKSTAR applied to the full N-body code GADGET-2. The subhalo velocity function and the correlation function of halos and subhalos are in good agreement. We investigate the effect of the chosen number density profile on the resulting subhalo clustering. Our simulation is approximate yet realistic and significantly faster compared to a full N-body simulation combined with a halo finder. The fast halo and subhalo clustering simulations offer good prospects for galaxy forward models using subhalo abundance matching.