A new theoretical framework for the unresolved resonance region

Neutron-nucleus cross sections can be divided into different energy ranges depending on the initial energy of the neutron. At lower energies, cross sections are characterised by peaks, called resonances, caused by the neutron being absorbed by the target nucleus. Positions and widths of these resonances cannot be predicted and must be measured. This energy range is called the “resolved resonance region” (RRR). Increasing further the energy of the neutron we reach a point where the resonances in the cross section are too close to each other to be resolved experimentally and we can only infer the average values of the widths and spacings between two adjacent resonances. This energy range is called the “unresolved resonance region” (URR).

Current computational methods treat the resonances in the URR using delta functions in place of full conditional cross section probability distributions to represent the probability of individual reaction channels (capture, elastic, fission), potentially missing more complex correlations between the channels. Moreover, this method does not allow to easily include information from different sources, for example from nuclear experiments. Additionally, for many applications we also need to know the cross sections at different temperatures and, thus, we need to properly account for the thermal motion of the target nuclei.

Due to these issues, we need to develop a theoretical framework that allows us to consistently treat the URR, the available experimental information, and the target thermal motion of the cross sections of neutron-induced reactions relevant for nuclear science and applications. This project aims to develop the required formalism using modern probabilistic and machine-learning approaches, reformulating the problem in terms of conditional probabilities. Bayesian networks and related machine-learning methods will be used to calculate cross-section probability density functions in a much faster way, enabling the combination of multiple probability distributions describing various physical effects.

This project will allow the student to acquire highly transferable skills in probabilistic modelling, statistical inference, and machine-learning techniques, with potential applications well beyond nuclear science, including data science and AI-related fields. Expertise in nuclear data and uncertainty quantification is highly desirable, making the student a strong candidate for both academic and applied research environments following completion of the project. The PhD student is also expected to collaborate closely with a number of UK and international partners, including opportunities for visits to the US national laboratories of Brookhaven and Lawrence Livermore, providing exposure to large-scale scientific projects and interdisciplinary research environments.

Open to candidates who pay UK/home rate fees. See UKCISA for further information.

You will need to meet the minimum entry requirements for our Physics PhD programme.