Utilizing Correlated Fission Observables

Fission is a complicated process that is widely beneficial in areas ranging from basic science calculations to applications. Inherently, the properties of the fission fragments, neutrons, and gamma-rays that are emitted during this process are highly correlated, and to study these correlations, it is useful to have event-by-event Monte Carlo models to describe the deexcitation of the fission fragments. One such code has been developed in T-2 at LANL, the Monte Carlo, Hauser-Feshbach fission code, CGMF. With this, average quantities for the fission fragments, prompt neutrons, and prompt gamma-rays can be calculated (e.g. energies and multiplicities) but correlations between observables can also be studied. In this talk, I will first discuss two recent additions to CGMF aimed at more accurately modeling sources of angular anisotropy and then discuss how we are utilizing energy correlations between fission fragments and prompt gamma rays to extract information about the prompt neutron multiplicity distribution. I will connect both of these projects to experimental efforts at LANL, and briefly mention how machine learning can be applied as a tool to assist in some of these efforts.