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Cited by 555

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

Statistical decay of very hot nuclei-the production of large clusters

Gross, D H E·1990·Reports on Progress in Physics

Abstract

Heavy ion collisions are the only tools that enable us to reach compressions of nuclear matter up to a factor of about three. Theories of the collision dynamics that are necessary in order to extract the equation of state for nuclear matter are discussed. The main part of the review is devoted to the statistical decay of equilibrised very hot nuclei. A new mathematical method, microcanonical Metropolis sampling, which allows the explicit calculation of microcanonical observables directly out of the microcanonical partition sum with high statistics, is presented. Thermodynamics of finite nuclei can thus be established from the first principles. This is important as nuclei subjected to long-range Coulomb forces do not allow extrapolation to the thermodynamic limit. This peculiarity has interesting consequences for the phase transitions and the critical behaviour of nuclei. It is also related to the property of nuclei to break into large clusters at high excitation.

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Crossref · 10.1088/0034-4885/53/5/003

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Metadata

Authors
Gross, D. H. E.
Venue
Reports on Progress in Physics
Published
1990-05-01
Volume
53
Issue
5
Pages
605-658
ISSN
  • 0034-4885 (ISSN-L)
  • 1361-6633
OpenAlex
W2021198715
Cited by
555
References
24
FWCI
17.83
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Statistical decay of very hot nuclei-the production of large clusters. https://app.cochat.ai/papers/10.1088/0034-4885/53/5/003
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Gross, D. H. E. (1990). Statistical decay of very hot nuclei-the production of large clusters. Reports on Progress in Physics. https://doi.org/10.1088/0034-4885/53/5/003

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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.