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.