Nuclear Reaction Data for Fission Products Off Stability
Neutron cross sections on fission products are relevant to a wide range of applications, including nuclear nonproliferation and forensics, spent-fuel assay, reactor burnup and design, as well as astrophysics. Evaluated nuclear data libraries generally fulfill application needs for isotopes on or near stability, however, for unstable fission products, theoretical descriptions of neutron-induced reactions often constitute the only available source of information. These models often make use of simplified assumptions, leading to unquantified impacts on predicted cross sections. In this work, we discuss possible approaches to addressing these issues, particularly by leveraging machine-learning methods, improved predictive reaction modeling, and experimental data to better constrain model parameters. Our goal is to eventually produce evaluated files for the most-produced nuclei off stability in the fission process of $^{235}$U and submit them to the ENDF/B for consideration in the future ENDF/B-IX.0 release. Here we present the methodology and discuss preliminary results comparing usual simplified approaches with a more realistic one accounting for nuclear deformation.