Gamma-ray tracking using graph neural networks

Sammanfattning: 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.