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Title: Searches for bound neutron-antineutron oscillation in liquid argon time projection chambers
Author: Hewes, Jeremy
ISNI:       0000 0004 7657 860X
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2018
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The next-generation Deep Underground Neutrino Experiment's liquid argon detector represents an opportunity to probe previously unexplored parameter space for beyond-Standard Model processes. One such process is baryon number violating neutron-antineutron oscillation, the observation of which would have profound implications on our understanding of the origin of the matter-antimatter asymmetry in the universe, and provide strong hints as to the nature of neutrino mass. A GENIE nnbar oscillation event generator was developed and officially released, taking into account various nuclear effects and final state interactions. Previous searches for the process are summarised, as are the sources of antiproton scattering used to derive nnbar branching ratios. The viability of machine learning image processing techniques to identify simulated signal nnbar events and reject potential atmospheric neutrino backgrounds in DUNE is explored. Images are produced using simulated nnbar and atmospheric neutrino events in DUNE, and a convolutional neural network is trained to distinguish the two. The network's ability to accept signal and reject background corresponds to a free nnbar lifetime sensitivity of 1.6e9 s at 90% confidence level, a factor of 5 improvement on the current limit from Super-Kamiokande. These machine learning techniques are applied to data from the on-surface MicroBooNE detector, and the network is found to be highly sensitive to differences between data and Monte Carlo (MC) simulations. Recommendations are made for further studies into the use of such techniques, and potential avenues for overcoming challenges in data-MC disagreement are presented.
Supervisor: Evans, Justin Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Particle physics ; Nucleon decay ; nnbar oscillation ; Liquid argon TPCs ; Machine learning