Informing Nuclear Data Evaluations by combining ML/AI and CoH Sampling, and Integrating the New Model into CGMF

Fission cross-sections are crucial in understanding nuclear reactions, for instance, in designing and analyzing nuclear reactors, applications in nuclear criticality safety, etc. Current challenges stem from inherent biases and uncertainties within existing fission models, limiting their predictive capabilities, and unknown systematic biases in experimental data. For model predictions we used the Hauser-Feshbach method implemented in the LANL code CoH code. CoH connects fission cross-sections with other reaction channels, and it also correlates fission cross-sections with prompt fission observables through CGMF, a fission-event generator.