Ahf: AMIGA'S HALO FINDER

By S. Knollmann, A. Knebe. 2009

672 citations recorded by the source corpus.

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.

Subfields: Astronomy and Astrophysics, Electrical and Electronic Engineering, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/f5542be63eb9fc9f8e002aadf0d019074d89677c

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Citations in the corpus

Testing gravity using cosmic voids (2014, methodology): “The dark matter haloes are identified using thesp rical overdensity code AHF (Knollmann & Knebe 2009).”
ESCAPE FRACTION OF IONIZING PHOTONS DURING REIONIZATION: EFFECTS DUE TO SUPERNOVA FEEDBACK AND RUNAWAY OB STARS (2014, methodology): “Dark matter (sub) halos are identified using the AMIGA halo finder (AHF, Gill et al. 2004; Knollmann & Knebe 2009).”
ELVIS: Exploring the Local Volume in Simulations (2013, methodology): “We also checked results at the final timestep (z = 0) against the public6, spherical overdensity-based Amiga Halo Finder (Knollmann & Knebe 2009) and found that the results did not differ significantly and were identical within the statistical variation of our sample of haloes.7 Three of the most us”
Forged in FIRE: cusps, cores and baryons in low-mass dwarf galaxies (2015, methodology): “For the halo identification in the simulation we have used the public code Amiga Halo Finder (AHF Knollmann & Knebe 2009), an MPI parallel code for finding gravitationally bound structures in simulations of cosmic structure.”
NEXUS: Tracing the cosmic web connection (2012, methodology): “The AHF halo and subhalo catalogues are complete up to haloes with 50 or more particles (for a complete description see Knollmann & Knebe 2009).”
The structure and dynamical evolution of the stellar disc of a simulated Milky Way-mass galaxy (2016, methodology): “We use the Amiga Halo Finder (AHF; Knollmann & Knebe 2009) to identify halos in the simulated box, where we adopt the time-dependent virial overdensity from Bryan & Norman (1998).”
NIHAO project – I. Reproducing the inefficiency of galaxy formation across cosmic time with a large sample of cosmological hydrodynamical simulations (2015, methodology): “Haloes in our zoom-in simulations were identified using the MPI+OpenMP hybrid halo finder AHF2 (Knollmann & Knebe 2009; Gill et al. 2004).”
Gusty, gaseous flows of FIRE: Galactic winds in cosmological simulations with explicit stellar feedback (2015, methodology): “We use the public distribution of AMIGA Halo Finder (AHF; Knollmann & Knebe 2009) to determine halo centres and measure central velocities using the adaptive mesh hierarchy method.”
The Local Group on FIRE: dwarf galaxy populations across a suite of hydrodynamic simulations (2018, methodology): “Our approach is similar to Wetzel et al. (2016), but we base our galaxy catalogues on AHF halo catalogues (rather than rockstar; Behroozi, Wechsler & Wu 2013) and the cuts placed on stellar particles vary slightly; most notably, Wetzel et al. (2016) did not include either our initial cut based on th”
The cosmic baryon cycle and galaxy mass assembly in the FIRE simulations (2016, methodology): “Dark matter halos are identified independently of the skid galaxy definition using the Amiga Halo Finder (AHF; Gill et al. 2004; Knollmann & Knebe 2009), where we use the evolving virial overdensity definition of Bryan & Norman (1998).”
Galaxy Groups Within 3500 km s−1 (2017, neutral): “In an N-body simulation, a group is synonymous with a virialized halo (Knollmann & Knebe 2009).”
Cosmological Simulations with Self-Interacting Dark Matter I: Constant Density Cores and Substructure (2012, methodology): “All of our halo catalogs and density profiles are derived using the publicly available code Amiga Halo Finder (AHF) (Knollmann & Knebe 2009).”
WHY BARYONS MATTER: THE KINEMATICS OF DWARF SPHEROIDAL SATELLITES (2012, methodology): “We use AHF (Knollmann & Knebe 2009; Gill et al. 2004) to identify halos and subhalos at every output step, and trace back the most massive progenitor of each satellite.”
Not so lumpy after all: modelling the depletion of dark matter subhaloes by Milky Way-like galaxies (2017, methodology): “We created halo catalogs and merger trees using AHF (Knollmann & Knebe 2011) and consistent-trees (Behroozi et al. 2013a), respectively.”
Absorption-line systems in simulated galaxies fed by cold streams (2011, methodology): “For this reason, we identify the satellites using the AMIGA halo finder (Gill, Knebe & Gibson 2004; Knollmann & Knebe 2009).”
Sweating the small stuff: simulating dwarf galaxies, ultra-faint dwarf galaxies, and their own tiny satellites (2015, methodology): “We use the Amiga Halo Finder (AHF, Knollmann & Knebe 2009) to identify gravitationally bound dark matter, stellar and gas particles in each snapshot of the simulation.”