COMPASO: A new halo finder for competitive assignment to spherical overdensities
Abstract
We describe a new method (COMPASO) for identifying groups of particles in cosmological N-body simulations. COMPASO builds upon existing spherical overdensity (SO) algorithms by taking into consideration the tidal radius around a smaller halo before competitively assigning halo membership to the particles. In this way, the COMPASO finder allows for more effective deblending of haloes in close proximity as well as the formation of new haloes on the outskirts of larger ones. This halo-finding algorithm is used in the ABACUSSUMMIT suite of N-body simulations, designed to meet the cosmological simulation requirements of the Dark Energy Spectroscopic Instrument (DESI) survey. COMPASO is developed as a highly efficient on-the-fly group finder, which is crucial for enabling good load-balancing between the GPU and CPU and the creation of high-resolution merger trees. In this paper, we describe the halo-finding procedure and its particular implementation in ABACUS, accompanying it with a qualitative analysis of the finder. We test the robustness of the COMPASO catalogues before and after applying the cleaning method described in an accompanying paper and demonstrate its effectiveness by comparing it with other validation techniques. We then visualize the haloes and their density profiles, finding that they are well fit by the NFW formalism. Finally, we compare other properties such as radius-mass relationships and two-point correlation functions with that of another widely used halo finder, ROCKSTAR.
- Publication:
-
Monthly Notices of the Royal Astronomical Society
- Pub Date:
- January 2022
- DOI:
- 10.1093/mnras/stab2980
- arXiv:
- arXiv:2110.11408
- Bibcode:
- 2022MNRAS.509..501H
- Keywords:
-
- methods: data analysis;
- galaxies: haloes;
- cosmology: theory;
- large-scale structure of Universe;
- Astrophysics - Cosmology and Nongalactic Astrophysics;
- Astrophysics - Instrumentation and Methods for Astrophysics
- E-Print:
- 22 pages, 12 figures, appendices, accepted in MNRAS