Producing Evaluation-quality

An evaluation of the average prompt fission neutron multiplicity, ν¯ p, of 239Pu(n,f) is shown. This evaluation includes (a) the correlated fission model CGMF, and (b) a detailed analysis of past and recently published experimental data. Using CGMF-calculated ν¯ pas prior enables to link, through the use of evaluated model input parameters, ν¯ p to other fission observables such as the prompt fission neutron spectrum (PFNS), pre-neutron emission fission yields as a function of mass, and the average total kinetic energy of the fragments. These evaluated parameters produce realistic predictions of many fission observables, while the evaluated ν¯ p agrees well (χ2 ≈ 1) with data. Moreover, with the new evaluated ν¯ p, the effective neutron multiplication factor of fast Pu ICSBEP critical assemblies are predicted with a mean bias of 58 pcm compared to 18 pcm with ENDF/B-VIII.0, when paired with a new 239Pu PFNS and fission cross section. Due to these encouraging validation results, the evaluated ν¯ p is currently part of a release candidate for the 239Pu ENDF/B-VIII.1 file. Hence, a correlated fission model was used for the first time for evaluating ν¯ p that is of evaluation quality. This is an important step towards consistent evaluations of prompt fission observables

The growth during infancy of parents and their children

The growth of infants up to two years of age has been compared with the growth, one generation earlier, of their parents during infancy. Up to the age of one year, the infant tended to follow the same pattern of growth as his mother during her infancy. There was no consistent correlation between the growth of the infant and that of his father during infancy. These findings show that there is no major genetic influence on weight during infancy, but it suggests that, in some manner, the mother repeats her own pattern of upbringing when rearing her child. This effect is not due to the time that non-breast milks or solid foods are introduced

Quantified uncertainties in fission yields from machine learning

As machine learning methods gain traction in the nuclear physics community, especially those methods that aim to propagate uncertainties to unmeasured quantities, it is important to understand how the uncertainty in the training data coming either from theory or experiment propagates to the uncertainty in the predicted values. Gaussian Processes and Bayesian Neural Networks are being more and more widely used, in particular to extrapolate beyond measured data. However, studies are typically not performed on the impact of the experimental errors on these extrapolated values. In this work, we focus on understanding how uncertainties propagate from input to prediction when using machine learning methods. We use a Mixture Density Network (MDN) to incorporate experimental error into the training of the network and construct uncertainties for the associated predicted quantities. Systematically, we study the effect of the size of the experimental error, both on the reproduced training data and extrapolated predictions for fission yields of actinides

Quantified uncertainties in fission yields from machine learning

As machine learning methods gain traction in the nuclear physics community, especially those methods that aim to propagate uncertainties to unmeasured quantities, it is important to understand how the uncertainty in the training data coming either from theory or experiment propagates to the uncertainty in the predicted values. Gaussian Processes and Bayesian Neural Networks are being more and more widely used, in particular to extrapolate beyond measured data. However, studies are typically not performed on the impact of the experimental errors on these extrapolated values. In this work, we focus on understanding how uncertainties propagate from input to prediction when using machine learning methods. We use a Mixture Density Network (MDN) to incorporate experimental error into the training of the network and construct uncertainties for the associated predicted quantities. Systematically, we study the effect of the size of the experimental error, both on the reproduced training data and extrapolated predictions for fission yields of actinides

Anisotropy in fission fragment and prompt neutron angular distributions

Several physics mechanisms can lead to the deviation from an isotropic angular distribution for both fission fragments and the neutrons that are emitted during the fission event. Two of these effects have recently been implemented into CGM