Abstract
By modeling the transition paths of the nuclear γ-decay cascade using a scale-free random network, we uncover a universal power-law distribution of γ-ray intensity ρ_{I}(I)∝I^{-2}, with I the γ-ray intensity of each transition. This property is consistently observed for all datasets with a sufficient number of γ-ray intensity entries in the National Nuclear Data Center database, regardless of the reaction type or nuclei involved. In addition, we perform numerical simulations that support the model's predictions of level population density.
Highlights
By modeling the transition paths of the nuclear γ-decay cascade using a scale-free random network, we uncover a universal power-law distribution of γ-ray intensity ρI ðIÞ ∝ I −2, with I the γ-ray intensity of each transition
In our earlier work [5], we showed that the index can be written as b 1⁄4 2T ex =T þ 1, where T ex is the excitation temperature in the plasma, and T is an atom-dependent constant related to the level density of the atom [6]
We show that such a process on a scale-free random acyclic network exhibits a universal power law ρI ðIÞ ∝ I −2, where I is the probability of passing a particular edge during the cascade and ρI is the number density of edges with the probability I
Summary
By modeling the transition paths of the nuclear γ-decay cascade using a scale-free random network, we uncover a universal power-law distribution of γ-ray intensity ρI ðIÞ ∝ I −2, with I the γ-ray intensity of each transition. This property is consistently observed for all datasets with a sufficient number of γ-ray intensity entries in the National Nuclear Data Center database, regardless of the reaction type or nuclei involved. [13,14] We show that such a process on a scale-free random acyclic network exhibits a universal power law ρI ðIÞ ∝ I −2 , where I is the probability of passing a particular edge during the cascade and ρI is the number density of edges with the probability I.
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