Cosmological simulations are the key tool for investigating the different processes involved in the formation of the universe from small initial density perturbations to galaxies and clusters of galaxies observed today. The identification and analysis of bound objects, halos, is one of the most important steps in drawing useful physical information from simulations. In the advent of larger and larger simulations, a reliable and parallel halo finder, able to cope with the ever-increasing data files, is a must. In this work we present the freely available MPI parallel halo finder Ahf. We provide a description of the algorithm and the strategy followed to handle large simulation data. We also describe the parameters a user may choose in order to influence the process of halo finding, as well as pointing out which parameters are crucial to ensure untainted results from the parallel approach. Furthermore, we demonstrate the ability of Ahf to scale to high resolution simulations.

We present a detailed comparison of fundamental dark matter halo properties retrieved by a substantial number of different halo finders. These codes span a wide range of techniques including friends‐of‐friends, spherical‐overdensity and phase‐space‐based algorithms. We further introduce a robust (and publicly available) suite of test scenarios that allow halo finder developers to compare the performance of their codes against those presented here. This set includes mock haloes containing various levels and distributions of substructure at a range of resolutions as well as a cosmological simulation of the large‐scale structure of the universe. All the halo‐finding codes tested could successfully recover the spatial location of our mock haloes. They further returned lists of particles (potentially) belonging to the object that led to coinciding values for the maximum of the circular velocity profile and the radius where it is reached. All the finders based in configuration space struggled to recover substructure that was located close to the centre of the host halo, and the radial dependence of the mass recovered varies from finder to finder. Those finders based in phase space could resolve central substructure although they found difficulties in accurately recovering its properties. Through a resolution study we found that most of the finders could not reliably recover substructure containing fewer than 30–40 particles. However, also here the phase‐space finders excelled by resolving substructure down to 10–20 particles. By comparing the halo finders using a high‐resolution cosmological volume, we found that they agree remarkably well on fundamental properties of astrophysical significance (e.g. mass, position, velocity and peak of the rotation curve). We further suggest to utilize the peak of the rotation curve, vmax, as a proxy for mass, given the arbitrariness in defining a proper halo edge.

We present a detailed comparison of the substructure properties of a single Milky Way sized dark matter halo from the Aquarius suite at five different resolutions, as identified by a variety of different (sub)halo finders for simulations of cosmic structure formation. These finders span a wide range of techniques and methodologies to extract and quantify substructures within a larger non‐homogeneous background density (e.g. a host halo). This includes real‐space‐, phase‐space‐, velocity‐space‐ and time‐space‐based finders, as well as finders employing a Voronoi tessellation, Friends‐of‐Friends techniques or refined meshes as the starting point for locating substructure. A common post‐processing pipeline was used to uniformly analyse the particle lists provided by each finder. We extract quantitative and comparable measures for the subhaloes, primarily focusing on mass and the peak of the rotation curve for this particular study. We find that all of the finders agree extremely well in the presence and location of substructure and even for properties relating to the inner part of the subhalo (e.g. the maximum value of the rotation curve). For properties that rely on particles near the outer edge of the subhalo the agreement is at around the 20 per cent level. We find that the basic properties (mass and maximum circular velocity) of a subhalo can be reliably recovered if the subhalo contains more than 100 particles although its presence can be reliably inferred for a lower particle number limit of 20. We finally note that the logarithmic slope of the subhalo cumulative number count is remarkably consistent and <1 for all the finders that reached high resolution. If correct, this would indicate that the larger and more massive, respectively, substructures are the most dynamically interesting and that higher levels of the (sub)subhalo hierarchy become progressively less important.

We examine the properties of satellites found in high‐resolution simulations of the Local Group (LG). We use constrained simulations designed to reproduce the main dynamical features that characterize the local neighbourhood, i.e. within tens of Mpc around the LG. Specifically, an LG‐like object is found located within the ‘correct’ dynamical environment and consisting of three main objects which are associated with the Milky Way, M31 and M33. By running two simulations of this LG from identical initial conditions – one with and one without baryons modelled hydrodynamically – we can quantify the effect of gas physics on the z= 0 population of subhaloes in an environment similar to our own. We find that above a certain mass cut, Msub > 2 × 108 h−1 M⊙ subhaloes in hydrodynamic simulations are more radially concentrated than those in simulations without gas. This is caused by the collapse of baryons into stars that typically sit in the central regions of subhaloes, making them denser. The increased central density of such a subhalo results in less mass loss due to tidal stripping than the same subhalo simulated with only dark matter. The increased mass in hydrodynamic subhaloes with respect to dark matter ones causes dynamical friction to be more effective, dragging the subhalo towards the centre of the host. This results in these subhaloes being effectively more radially concentrated than their dark matter counterparts.

This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©2011 RAS © 2011 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.Using a statistical sample of dark matter haloes drawn from a suite of cosmological N-body simulations of the cold dark matter (CDM) model, we quantify the impact of a simulated halo's mass accretion and merging history on two commonly used measures of its dynamical state, the virial ratio η and the centre of mass offset Δr. Quantifying this relationship is important because the degree to which a halo is dynamically equilibrated will influence the reliability with which we can measure characteristic equilibrium properties of the structure and kinematics of a population of haloes. We begin by verifying that a halo's formation redshift zform correlates with its virial mass Mvir and we show that the fraction of its recently accreted mass and the likelihood of it having experienced a recent major merger increase with increasing Mvir and decreasing Zform. We then show that both η and Δr increase with increasing Mvir and decreasing zform, which implies that massive recently formed haloes are more likely to be dynamically unrelaxed than their less massive and older counterparts. Our analysis shows that both η and Δr are good indicators of a halo's dynamical state, showing strong positive correlations with recent mass accretion and merging activity, but we argue that Δr provides a more robust and better defined measure of dynamical state for use in cosmological N-body simulations at z≃ 0. We find that Δr≲ 0.04 is sufficient to pick out dynamically relaxed haloes at z= 0. Finally, we assess our results in the context of previous studies, and consider their observational implicationsAK is supported by the Spanish Ministerio de Ciencia e Innovación (MICINN) in Spain through the Ramon y Cajal programme as well as the grants AYA 2009-13875-C03-02, AYA2009-12792-C03-03, CSD2009- 00064 and CAMS2009/ESP-1496. He acknowledges support by the MICINN under the Consolider-Ingenio, SyeC project CSD- 2007-0005