Enhancing water Cherenkov detector technology with machine learning techniques applied at a test beam experiment

Neutrino oscillation experiments have entered a new era of precision measurements. With ongoing experiments detecting the first evidence of CP symmetry violation in leptons, now future experiments, such as the Hyper-Kamiokande experiment under construction in Japan, aim to establish CP violation in neturino oscillations through the first measurement of the neutrino mixing CP phase. To achieve this goal, upgraded hardware and new detector capabilities must be matched by advances in analysis techniques. The Water Cherenkov Test Experiment (WCTE), due to be operated at CERN in mid 2023, will act as a test-bed for novel detector technologies and analysis techniques for water-based particle detectors. This fellowship will involve developing machine learning based event reconstruction software for complex multi-ring events in the WCTE, taking a leading role in the commissioning and operation of the detector, collecting experimental data in a charged pion test beam to use in validating and further developing the reconstruction software, and applying these advances to novel detector calibration methods.