Fitting the cross-section probability tables with symbolic regression techniques

Below 1 MeV incident energy, cross sections for neutrons interacting with nuclei show significant fluctuations that are not predictable. The current methodology used to describe such behavior and construct the probability table of the cross section is based on the extrapolation of the average resonance widths and average resonance spacings from the resonance region and generate Monte Carlo realizations of resonance ladders. We then use these realizations to construct the probability tables. Although this is a standard and widely used technique, it is computationally very expensive. Our goal is to produce accurate analytical fits of these tables adopting a machine learning approach. For this project we generate the zero-temperature probability distribution function for specific reactants of interest and use symbolic regression techniques to produce an analytical fit of the probabilities that can be used in real life applications with a considerable speed up of the computational time.