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The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

AHF: AMIGA'S HALO FINDER

Open access
Knollmann, Steffen R., Knebe, Alexander·2009·The Astrophysical Journal Supplement Series

Abstract

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.

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Crossref · 10.1088/0067-0049/182/2/608

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Metadata

Authors
Knollmann, S. R., & Knebe, A.
Venue
The Astrophysical Journal Supplement Series
Published
2009-05-19
Volume
182
Issue
2
Pages
608-624
ISSN
  • 0067-0049 (ISSN-L)
  • 1538-4365
OpenAlex
W2042956136
Cited by
889
References
45
FWCI
20.56
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: AHF: AMIGA'S HALO FINDER. https://app.cochat.ai/papers/10.1088/0067-0049/182/2/608
The paper
Knollmann, S. R., & Knebe, A. (2009). AHF: AMIGA'S HALO FINDER. The Astrophysical Journal Supplement Series. https://doi.org/10.1088/0067-0049/182/2/608

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.