Adaptive Refinement Tree: A New High‐ResolutionN‐Body Code for Cosmological Simulations

458 indexed citations

Abstract

loading...

About

This paper, published in 1997, received 458 indexed citations. Written by Andrey V. Kravtsov, Anatoly Klypin and A. M. Khokhlov covering the research area of Statistical and Nonlinear Physics, Instrumentation and Astronomy and Astrophysics. It is primarily cited by scholars working on Astronomy and Astrophysics (423 citations), Instrumentation (192 citations) and Nuclear and High Energy Physics (86 citations). Published in The Astrophysical Journal Supplement Series.

In The Last Decade

doi.org/10.1086/313015 →

Countries where authors are citing Adaptive Refinement Tree: A New High‐ResolutionN‐Body Code for Cosmological Simulations

Specialization
Citations

This map shows the geographic impact of Adaptive Refinement Tree: A New High‐ResolutionN‐Body Code for Cosmological Simulations. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Adaptive Refinement Tree: A New High‐ResolutionN‐Body Code for Cosmological Simulations with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adaptive Refinement Tree: A New High‐ResolutionN‐Body Code for Cosmological Simulations more than expected).

Fields of papers citing Adaptive Refinement Tree: A New High‐ResolutionN‐Body Code for Cosmological Simulations

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Adaptive Refinement Tree: A New High‐ResolutionN‐Body Code for Cosmological Simulations. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Adaptive Refinement Tree: A New High‐ResolutionN‐Body Code for Cosmological Simulations.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1086/313015.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026