The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Adaptive Refinement Tree: A New High‐Resolution N‐Body Code for Cosmological Simulations
Open accessAbstract
We present a new high-resolution N-body algorithm for cosmological simulations. The algorithm employs a traditional particle-mesh technique on a cubic grid and successive multilevel relaxations on the finer meshes, introduced recursively in a fully adaptive manner in the regions where the density exceeds a predefined threshold. The mesh is generated to effectively match an arbitrary geometry of the underlying density field -- a property particularly important for cosmological simulations. In a simulation the mesh structure is not created at every time step but is properly adjusted to the evolving particle distribution. The algorithm is fast and effectively parallel. We present a detailed description of the methodology, implementation, and tests of the code. We further use the code to study the structure of dark matter halos in high-resolution (2/h kpc) simulations of standard CDM (Omega=1, h=0.5, sigma_8=0.63) and LCDM (Omega_Lambda=1-Omega_0=0.7, h=0.7, sigma_8=1.0) models. We find that halo density profiles in both CDM and LCDM models are well fitted by the analytical model presented recently by Navarro et al., which predicts a singular [$ρ(r)\propto r^{-1}$] behavior of the halo density profiles at small radii. We therefore conclude that halos formed in the $Λ$CDM model have structure similar to CDM halos and thus cannot explain the dynamics of the central parts of dwarf spiral galaxies, as inferred from the galaxies' rotation curves.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Universal Density Profile from Hierarchical Clustering
Navarro, J. F., Frenk, C. S., & White, S. D. M. (1997) · The Astrophysical Journal · doi:10.1086/304888 · Cited by 9,917
The Structure of Cold Dark Matter Halos
Navarro, J. F., Frenk, C. S., & White, S. D. M. (1996) · The Astrophysical Journal · doi:10.1086/177173 · Cited by 8,544 · cited 1×
“20) presented by Navarro et al. (1996a) and have cuspy ( ρ ( r ) ∝ r − 1 ) structure in the central ( r < 10 h − 1 kpc) parts of a halo with no indications of a core down to the resolution limit of our simulations.”
Background
Cited by
The cosmological simulation code gadget-2
Springel, V. (2005) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2005.09655.x · Cited by 6,368
Where Are the Missing Galactic Satellites?
Klypin, A., Kravtsov, A. V., Valenzuela, O., & Prada, F. (1999) · The Astrophysical Journal · doi:10.1086/307643 · Cited by 2,741
Self-citationTHE AVERAGE STAR FORMATION HISTORIES OF GALAXIES IN DARK MATTER HALOS FROM z = 0-8
Behroozi, P. S., Wechsler, R. H., & Conroy, C. (2013) · The Astrophysical Journal · doi:10.1088/0004-637x/770/1/57 · Cited by 2,167
UniverseMachine: The correlation between galaxy growth and dark matter halo assembly from z = 0−10
Behroozi, P., Wechsler, R. H., Hearin, A. P., & Conroy, C. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz1182 · Cited by 1,243
MultiDark simulations: the story of dark matter halo concentrations and density profiles
Klypin, A., Yepes, G., Gottlöber, S., Prada, F., & Heß, S. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw248 · Cited by 970
Self-citation
Metadata
- Authors
- Kravtsov, A. V., Klypin, A. A., & Khokhlov, A. M.
- Venue
- The Astrophysical Journal Supplement Series
- Publisher
- American Astronomical Society
- Published
- 1997-07
- Volume
- 111
- Issue
- 1
- Pages
- 73-94
- ISSN
- 0067-0049 (ISSN-L)
- 1538-4365
- OpenAlex
- W2161078062
- Cited by
- 669
- References
- 2
- FWCI
- 12.50
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Adaptive Refinement Tree: A New High‐Resolution N‐Body Code for Cosmological Simulations. https://app.cochat.ai/papers/10.1086/313015
- The paper
- Kravtsov, A. V., Klypin, A. A., & Khokhlov, A. M. (1997). Adaptive Refinement Tree: A New High‐Resolution N‐Body Code for Cosmological Simulations. The Astrophysical Journal Supplement Series. https://doi.org/10.1086/313015
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.