Bayesian uncertainty quantification for synthesizing superheavy elements
Yueping Fang, Zepeng Gao, Yinu Zhang, Zehong Liao, Yu Yang, Jun Su,, Long Zhu

TL;DR
This paper applies Bayesian uncertainty quantification to improve predictions of evaporation residue cross sections for synthesizing superheavy elements, constraining key model parameters with data and analyzing their correlations.
Contribution
It introduces a Bayesian framework to quantify uncertainties in DNS model parameters, enhancing the predictive accuracy for superheavy element synthesis.
Findings
Optimal incident energies are weakly dependent on fission process.
Model parameters are strongly correlated, making independent uncertainty propagation unreasonable.
Predicted ERCS and optimal energies for Z=119 synthesis reactions.
Abstract
To improve the theoretical prediction power for synthesizing superheavy elements beyond Og, a Bayesian uncertainty quantification method is employed to evaluate the uncertainty of the calculated evaporation residue cross sections (ERCS) for the first time. The key parameters of the dinuclear system (DNS) model, such as the diffusion parameter , the damping factor , and the level-density parameter ratio are systematically constrained by the Bayesian analysis of recent ERCS data. One intriguing behavior is shown that the optimal incident energies (OIE) corresponding to the largest ERCS weakly depend on the fission process. We also find that these parameters are strongly correlated and the uncertainty propagation considering the parameters independently is not reasonable. The 2 confidence level of posterior distributions for $a…
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Taxonomy
TopicsNuclear reactor physics and engineering · Nuclear Materials and Properties · Graphite, nuclear technology, radiation studies
