The halo-finding problem revisited: a deep revision of the ASOHF code

Abstract

Context. New-generation cosmological simulations are providing huge amounts of data, whose analysis becomes itself a pressing computational problem. In particular, the identification of gravitationally bound structures, known as halo finding, is one of the main analyses. Several codes that were developed for this task have been presented during the past years. Aims. We present a deep revision of the code ASOHF. The algorithm was thoroughly redesigned in order to improve its capabilities of finding bound structures and substructures using both dark matter particles and stars, its parallel performance, and its abilities of handling simulation outputs with vast amounts of particles. This upgraded version of ASOHF is conceived to be a publicly available tool. Methods. A battery of idealised and realistic tests are presented in order to assess the performance of the new version of the halo finder.Results. In the idealised tests, ASOHF produces excellent results. It is able to find virtually all the structures and substructures that we placed within the computational domain. When the code is applied to realistic data from simulations, the performance of our finder is fully consistent with the results from other commonly used halo finders. The performance in substructure detection is remarkable. In addition, ASOHF is extremely efficient in terms of computational cost. Conclusions. We present a publicly available deeply revised version of the ASOHF halo finder. The new version of the code produces remarkable results in terms of halo and subhalo finding capabilities, parallel performance, and low computational cost. Key words. large-scale structure of the Universe -dark matter -galaxies: clusters: general -galaxies: halos -methods: numerical 1 Friends of friends. 2 Spherical overdensity. 3 Bound density maxima. 4 Amiga halo finder. 5 Adaptive spherical overdensity halo finder. 6 Hierarchical bound tracing.

On the choice of the most suitable indicator for the assembly state of dark matter haloes through cosmic time

“…For each snapshot of the simulation, we have identified the DM haloes using the public halo finder ASOHF (Planelles & Quilis 2010;Knebe et al 2011;Vallés-Pérez et al 2022) 1 , which is based on the spherical-overdensity definition and uses the virial radius (according to the prescription of Bryan & Norman 1998) to delimit the extent of the haloes that are not substructure.…”

“…In this work, we have tested the three possible combinations between the minimum of gravitational potential (defined as the location of the most-bound DM particle, as obtained by ASOHF and described in detail in Vallés-Pérez et al 2022), the DM density peak, and the DM centre-of-mass. We find that the most robust results are obtained for the Peak-CM pair.…”

“…While 𝑓 sub should naturally correlate with the assembly state (especially, with the merging state), its interpretation is very subtle due to several factors. First, there is not a unique way to define the extent of a subhalo, and differences amongst halo finders have a dramatic impact on the recovered masses of substructures (see Vallés-Pérez et al 2022, their figures 5 and 10). In second place, the amount of substructure produced in simulations depends strongly, not only on resolution, but also on the numerical scheme employed to solve gravity.…”

“…Kimmig et al 2022). While the election of this magnitude is well-motivated, the mass contained in substructures in simulated haloes depends critically on numerical resolution and the precise definition of the substructure extent (see, e.g, the discussion in Vallés-Pérez et al 2022), making this criterion less comparable.…”

A Conditional Abundance Matching Method of Extending Simulated Halo Merger Trees to Resolve Low-Mass Progenitors and Sub-halos

“…There is a vast literature in each of the steps. Examples of the structure finders include those based on the overdensity set obtained with boundary growing and pruning Boylan-Kolchin et al 2009;Planelles & Quilis 2010;Vallés-Pérez et al 2022), and those based on direct link of particles (Davis et al 1985;Diemand et al 2006;Behroozi et al 2012). Examples of tree builders include Monte Carlo methods based on the extended Press-Schechter (EPS) formalism (Somerville & Kolatt (1999); Cole et al (2000); Parkinson et al (2007); Somerville et al (2008); Zhang et al (2008), see also Jiang & van den Bosch (2014) for a review), those based on linking simulated (sub)halos Boylan-Kolchin et al 2009;Han et al 2012;Behroozi et al 2013;, and those based on post-processing and homogenizing trees produced by other methods (Helly et al 2003;.…”