While there are existing methods of gamma ray-track reconstruction in specialized detectors such as AGATA, including backtracking and clustering, it is naturally of interest to diversify the portfolio of available tools to provide us viable alternatives. In this study some possibilities found in the field of machine learning were investigated, more specifically within the field of graph neural networks. In this project there was attempt to reconstruct gamma tracks in a germanium solid using data simulated in Geant4. The data consists of photon energies below the pair production limit and so we are limited to the processes of photoelectric absorption and Compton scattering. The author turned to the field of graph networks to utilize its edge and node structure for data of such variable input size as found in this task. A graph neural network (GNN) was implemented and trained on a variety of gamma multiplicities and energies and was subsequently tested in terms of various accuracy parameters and generated energy spectra. In the end the best result involved an edge classifier trained on a large dataset containing a 10^6 tracks bundled together into separate events to be resolved. The network was capable of recalling up to 95 percent of the connective edges for the selected threshold in the infinite resolution case with a peak-to-total ratio of 68 percent for a set of packed data with a model trained on simulated data including realistic uncertainties in both position and energy. / Trots att det existerar en mängd metoder för rekonstruktion av spår i specialiserade detektorer som AGATA är det av naturligt intresse att diversifiera och undersöka nya verktyg för uppgiften. I denna studie undersöktes några möjligheter inom maskininlärning, närmare bestämt inom området neurala grafnätverk.  Under projektets gång simulerades data av fotoner i en ihålig, sfärisk geometri av germanium i Geant4. Den simulerade datan är begränsad till energier under parproduktion så datan består av reaktioner genom den fotoelektriska effekten och comptonspridning. Den variabla storleken på denna data och dess spridning i detektorns geometri lämpar sig för ett grafformat med nod och länkstruktur. Ett neuralt grafnätverk (GNN) implementerades och tränades på data med gamma av variabel multiplicitet och energi och evaluerades på ett urval prestandaparametrar och dess förmåga att generera ett användbart spektra. Slutresultatet involverade en länkklassificerings modell tränat på data med 10^6 spår sammanslagna till händelser. Nätverket återkallade 95 procent av positiva länkar för ett val av tröskelvärde i fallet med oändlig upplösning med ett peak-to-total på 68 procent för packad data behandlad med osäkerhet i energi och position motsvarande fallet med begränsad upplösning.

Features recently added to the nuclear reaction software TALYS allow the use of the GEF model as a fission fragment generator. GEF generates data for fission fragment yields, total excitation energy (TXE), total kinetic energy (TKE) and individual fragment excitation energies (E*) with their standard deviations through Monte Carlo simulations for TALYS. In this work a framework named McPUFF was developed to couple GEF and TALYS and study the propagation of uncertainties in fission fragment data. The GEF model has a set of 94 parameters which were changed in order to produce perturbed output data. Both GEF and TALYS were modified to allow implementation of the Total Monte Carlo (TMC) method which is a method for handling the propagation of uncertainties throughout the simulation process. The developed framework allows the user to control aspects of the nuclear reaction using a set of input files. It is designed to be fast and memory efficient, performing simulations in parallel and storing all results in an object structure. A demonstration of the framework for the neutron induced fission of 235U, 238U and 239Pu was performed. Randomly perturbed sets of fission fragment data were created by GEF and fed into TALYS for simulation of the evaporation process using a Hauser-Feshbach statistical model. The impact that the perturbation of parameters in GEF has on results from TALYS were investigated for prompt particle multiplicity and energy. The results showed that a perturbation of parameter values in GEF by 3 percent has significant effects on values for fission observables produced by TALYS. The TALYS results for 235U showed an uncertainty for prompt neutron multiplicity of σn = 0.16 neutrons with an uncertainty for the neutron energy of σϵn = 0.03 MeV. The corresponding values for the uncertainty of the prompt γ-ray multiplicity were σγ = 0.10 γ-rays with an uncertainty for the γ-ray energy of σϵγ = 0.02 MeV. An investigation of how changes in the angular momentum of the fission fragments affects the evaporation process in GEF and TALYS was performed through the perturbation of the GEF parameter Jscaling. The results highlighted the need to scrutinize the handling of angular momentum in TALYS.

<p>Accelerator-driven systems for transmutation of nuclear waste have been suggested as a means for dealing with spent fuel components that pose potential radiological hazard for long periods of time. While not entirely removing the need for underground waste repositories, this nuclear waste incineration technology provides a viable method for reducing both waste volumes and storage times. Potentially, the time spans could be diminished from hundreds of thousand years to merely 1.000 years or even less. A central aspect for accelerator-driven systems design is the prediction of safety parameters and fuel economy. The simulations performed rely heavily on nuclear data and especially on the precision of the neutron cross section representations of essential nuclides over a wide energy range, from the thermal to the fast energy regime. In combination with a more demanding neutron flux distribution as compared with ordinary light-water reactors, the expanded nuclear data energy regime makes exploration of the cross section sensitivity for simulations of accelerator-driven systems a necessity. This fact was observed throughout the work and a significant portion of the study is devoted to investigations of nuclear data related effects. The computer code package EA-MC, based on 3-D Monte Carlo techniques, is the main computational tool employed for the analyses presented. Directly related to the development of the code is the extensive IAEA ADS Benchmark 3.2, and an account of the results of the benchmark exercises as implemented with EA-MC is given. CERN's Energy Amplifier prototype is studied from the perspectives of neutron source types, nuclear data sensitivity and transmutation. The commissioning of the n_TOF experiment, which is a neutron cross section measurement project at CERN, is also described.</p>

Accelerator-driven systems for transmutation of nuclear waste have been suggested as a means for dealing with spent fuel components that pose potential radiological hazard for long periods of time. While not entirely removing the need for underground waste repositories, this nuclear waste incineration technology provides a viable method for reducing both waste volumes and storage times. Potentially, the time spans could be diminished from hundreds of thousand years to merely 1.000 years or even less. A central aspect for accelerator-driven systems design is the prediction of safety parameters and fuel economy. The simulations performed rely heavily on nuclear data and especially on the precision of the neutron cross section representations of essential nuclides over a wide energy range, from the thermal to the fast energy regime. In combination with a more demanding neutron flux distribution as compared with ordinary light-water reactors, the expanded nuclear data energy regime makes exploration of the cross section sensitivity for simulations of accelerator-driven systems a necessity. This fact was observed throughout the work and a significant portion of the study is devoted to investigations of nuclear data related effects. The computer code package EA-MC, based on 3-D Monte Carlo techniques, is the main computational tool employed for the analyses presented. Directly related to the development of the code is the extensive IAEA ADS Benchmark 3.2, and an account of the results of the benchmark exercises as implemented with EA-MC is given. CERN's Energy Amplifier prototype is studied from the perspectives of neutron source types, nuclear data sensitivity and transmutation. The commissioning of the n_TOF experiment, which is a neutron cross section measurement project at CERN, is also described.