In nuclear archaeology, we are interested in original irradiation parameters of a nuclear reactor. It is possible to calculate these parameters with knowledge of the reactor design and forensic isotopic composition measurements. However, this analysis entails solving an inverse problem which poses a challenging task. One approach for this problem is the calculation of a posterior distribution of the parameters with the Machine Learning technique “Markov Chain Monte Carlo”. This method has proven useful in the past but several challenges occur which might be tackled by applying a special type of neural networks, conditional invertible neural networks, to the problem. The thesis will focus on the implementation of both techniques and a comprehensive comparison of them in order to assess advantages of using either of them in an nuclear archaeology context.

Future reactor concepts, such as Small Modular Reactors (SMRs) pose new challenges for traditional non-proliferation safeguards. The goal of this research is improving safegards for SMRs using antineutrinos to verify that a reactor is only used for civilian purposes. As part of BSc thesis, this project will explore the positioning and properties of antineutrino detectors for determining reactor characteristics and/or explore the significance of neutrino oscillations, including a hypothetical sterile neutrino. The student will learn about the importance of safeguards, SMR technology and the physics of nuclear power plants. The student will be using Python in combination with simulation tools, e.g. GEANT4 or OpenMC.

Nuclear Resonance Fluorescence (NRF) is a process in which a nucleus is excited after absorbing a gamma ray and subsequently de-excites through the emission of one or more resonant gamma rays. The energy of the emitted gamma ray(s) depends on the distribution of the nuclear energy levels and can therefore be used as a signature to identify specific elements and even isotopes. Samples can thus be probed by using a photon beam to excite the nuclei present in them and by detecting the gamma rays thereby scattered or transmitted. Since the energies of the gamma rays are high enough to travel through shielding materials, this technique can be used to non-destructively study the interior composition of objects. In nuclear disarmament and non-proliferation verification this presents the potential to inspect objects such as canisters, warheads or facilities. This project focuses on the development and application of a Monte Carlo simulation framework to study the use of NRF in relevant verification scenarios.

The issue of nuclear non-proliferation has become more important in recent years, as for example a number of states are actively pursuing the acquisition of nuclear propulsion for their navies. The sensitive environment of these boats renders the application of traditional safeguards difficult. A potential novel safeguarding technique involves the use of antineutrinos, which can be used to safeguard the reactor from stand-off distances. In this project, the student will validate a Brazilian design of a naval reactor from the public literature, as well as its verification signatures, like its neutron and neutrino spectrum, emitted from the reactor during operation. The reactor is modelled using OpenMC, an open-source software for reactor simulation. This will help to get a deeper understanding of the origin of this emission from special types of reactors, like naval reactors. The student should have a basic knowledge of Python.