Nuclear reaction network unveils novel reaction patterns based on stellar energies

Chunheng Jiang, Boleslaw K. Szymanski, Jie Lian, Shlomo Havlin, Jianxi Gao

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Abstract

Despite the advances in discovering new nuclei, modeling microscopic nuclear structure, nuclear reactors, and stellar nucleosynthesis, we still lack a systemic tool, such as a network approach, to understand the structure and dynamics of over 70 thousands reactions compiled in JINA REACLIB. To this end, we develop an analysis framework, under which it is simple to know which reactions generally are possible and which are not, by counting neutrons and protons incoming to and outgoing from any target nucleus. Specifically, we assemble here a nuclear reaction network in which a node represents a nuclide, and a link represents a direct reaction between nuclides. Interestingly, the degree distribution of nuclear network exhibits a bimodal distribution that significantly deviates from the common power-law distribution of scale-free networks and Poisson distribution of random networks. Based on the dynamics from the cross section parameterizations in REACLIB, we surprisingly find that the distribution is universal for reactions with a rate below the threshold, λ < e-Tγ, where T is the temperature and γ ≈ 1.05. Moreover, we discover three rules that govern the structure pattern of nuclear reaction network: (i) reaction-type is determined by linking choices, (ii) network distances between the reacting nuclides on 2D grid of Z vs N of nuclides are short, and (iii) each node in- and out-degrees are close to each other. By incorporating these three rules, our model universally unveils the underlying nuclear reaction patterns hidden in a large and dense nuclear reaction network regardless of nuclide chart expansions. It enables us to predict missing links that represent possible new nuclear reactions not yet discovered.

Original languageEnglish
Article number083035
JournalNew Journal of Physics
Volume23
Issue number8
DOIs
StatePublished - Aug 2021

Bibliographical note

Publisher Copyright:
© 2021 The Author(s).

Funding

We acknowledge the support of the US National Science Foundation under Grant No. 2047488. SH wishes to thank the Israel Science Foundation, the NSF-BSF (Grant No. 2019740), the EU H2020 project RISE (Project No. 821115), the EU H2020 DIT4TRAM, and DTRA (Grant No. HDTRA-1- 19-1-0016), the PAZY foundation for financial support. BKS research was supported by the Army Research Office, Grant W911NF-16-1-0524, and the Office of Naval Research, Grant N00014-15-1-26.

FundersFunder number
EU H2020821115
EU H2020 DIT4TRAMHDTRA-1- 19-1-0016
NSF-BSF2019740
National Science Foundation2047488
Office of Naval ResearchN00014-15-1-26
Army Research OfficeW911NF-16-1-0524
Israel Science Foundation
PAZY Foundation

    Keywords

    • Bimodal degree distribution
    • Network model
    • Network reconstruction
    • Network science
    • Nuclear reaction

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