Connecting the Dots, or Nuclear Data in the Age of Supercomputing

摘要

Recent increases in computing power have allowed for much progress to be made in the field of nuclear data. The advances listed below are each significant, but together bring the potential to completely change our perspective on the nuclear data evaluation process. The use of modern nuclear modeling codes like TALYS and the Monte Carlo sampling of its model parameter space, together with a code system developed at NRG Petten, which automates the production of ENDF-6 formatted files, their processing, and their use in nuclear reactor calculations, constitutes the Total Monte Carlo approach, which directly links physical model parameters with calculated integral observables like k_(eff). Together with the Backward-Forward Monte Carlo method for weighting samples according their statistical likelihood, the Total Monte Carlo can be applied to complete isotopic chains in a consistent way, to simultaneously evaluate nuclear data and the associated uncertainties in the continuum region. Another improvement is found in the uses of microscopic models for nuclear reaction calculations. For example, making use of QRPA excited states calculated with the Gogny interaction to solve the long standing question of the origin of the ad hoc "pseudo-states" that are introduced in evaluated nuclear data files to account for the Livermore pulsed sphere experiments. A third advance consists of the recent optimization of the Gogny D1M effective nuclear interaction, including constraints from experimental nuclear masses at the "beyond the mean field" level. All these advances are only made possible by the availability of vast resources of computing power, and even greater resources will allow connecting them, going continuously from the parameters of the nuclear interaction to reactor calculations. However, such scheme will surely only be usable for applications if a few fine-tuning "knobs" are introduced in it. The values of these adjusted parameters will have to be calibrated versus differential and integral experimental constraints.