Neutrinoless Double Beta Decay(0𝜈𝛽𝛽) is one of the major research interests in neutrino physics. The discovery of 0𝜈𝛽𝛽 would answer persistent puzzles in the Standard Model of Elementary Particles. KamLAND-Zen is one of the leading efforts in the search of 0𝛽𝛽 and has acquired data from 745 kg of ^{136}Xe over 224 live-days. This data is analyzed using a Bayesian approach consisting of a Markov Chain Monte Carlo (MCMC) algorithm. The implementation of the Bayesian analysis, which is the focal point of this dissertation, yields a 90\% Credible Interval at T^{0𝜈}_{1/2} = 7.03 × 10^{25} years. Finally, a machine learning event classification algorithm, based on a spherical convolutional neural network (spherical CNN) was developed to increase the T^{0𝜈}_{1/2} sensitivity. The classification power of this algorithm was demonstrated on a Monte Carlo detector simulation, and a data driven classifier was trained to reject crucial backgrounds in the 0𝜈𝛽𝛽 analysis. After implementing the spherical CNN, an increase in T^{0𝜈}_{1/2} sensitivity of 11.0% is predicted. These early studies pave the way for substantial improvements in future 0𝜈𝛽𝛽 analyses.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/41491 |
| Date | 30 September 2020 |
| Creators | Li, Aobo |
| Contributors | Grant, Christopher P. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-NonCommercial-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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