Bayesian evaluation of energy dependent neutron induced fission yields

From both the fundamental and applied perspectives, fragment mass distributions are important observablesof fission. We apply the Bayesian neural network (BNN) approach to learn the existing neutron induced fissionyields and predict unknowns with uncertainty quantification. Comparing the predicted results with experimentaldata, the BNN evaluation results are found to be satisfactory for the distribution positions and energy dependenciesof fission yields. Predictions are made for the fragment mass distributions of several actinides, which may beuseful for future experiments.

Modeling complex networks of nuclear reaction data for probing their discovery processes

Hundreds of thousands of experimental data sets of nuclear reactions have been systematically collected,and their number is still growing rapidly.The data and their correlations compose a complex system,which underpins nuclear science and technology.We model the nuclear reaction data as weighted evolving networks for the purpose of data verification and validation.The networks are employed to study the growing cross-section data of a neutron induced threshold reaction(n,2n)and photoneutron reaction.In the networks,the nodes are the historical data,and the weights of the links are the relative deviation between the data points.It is found that the networks exhibit small-world behavior,and their discovery processes are well described by the Heaps law.What makes the networks novel is the mapping relation between the network properties and the salient features of the database:the Heaps exponent corresponds to the exploration efficiency of the specific data set,the distribution of the edge-weights corresponds to the global uncertainty of the data set,and the mean node weight corresponds to the uncertainty of the individual data point.This new perspective to understand the database will be helpful for nuclear data analysis and compilation.