Identifying Inconsistencies in Fission Product Yield Evaluations with Prompt Neutron Emission

Constraining Fission Yields Using Machine Learning

Having accurate measurements of fission observables is important for a variety of applications, ranging from energy to non-proliferation, defense to astrophysics. Because not all of these data can be measured, it is necessary to be able to accurately calculate these observables as well. In this work, we exploit Monte Carlo and machine learning techniques to reproduce mass and kinetic energy yields, for phenomenological models and in a model-free way. We begin with the spontaneous fission of 252Cf, where there is abundant experimental data, to validate our approach, with the ultimate goal of creating a global yield model in order to predict quantities where data are not currently available.

Prompt and Delayed Neutron Emissions and Fission Product Yield Calculations with Hauser-Feshbach Statistical Decay Theory and Summation Calculation Method

We demonstrate the neutron emission and fission product yield calculations using the Hauser–Feshbach Fission Fragment Decay (HF3D) model and β decay. The HF3D model calculates the statistical decay of more than 500 primary fission fragment pairs formed by the neutron induced fission of 235U. In order to calculate the prompt neutron and photon emissions, the primary fission fragment distributions, i.e. mass, charge, excitation energy, spin and parity are deterministically generated and numerically integrated for all fission fragments. The calculated prompt neutron multiplicities, independent fission product yield are fully consistent each other. We combine the β-decay and the summation calculations with the HF3D model calculation to obtain the cumulative fission product yield, decay heat and delayed neutron yield. The calculated fission observables are compared with available experimental data.