Deep Learning for the KamLAND-Zen Search for 0๐๐ฝ๐ฝ
Name
fraker-sfraker-sm-physics-2022-thesis.pdf
Description
Thesis PDF
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10.44 MB
Format
Adobe PDF
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4011275f310b1cb4ac65ea964d4f4834
Author(s)
Fraker, Suzannah
Advisor(s)
Winslow, Lindley
Date Issued
February 2022
Publisher
Massachusetts Institute of Technology
Abstract
Neutrinoless double beta decay (0๐๐ฝ๐ฝ) is a major interest in neutrino physics. Discovery of 0๐๐ฝ๐ฝ would demonstrate that neutrinos are Majorana fermions and that lepton number is not a symmetry of nature, thus providing a possible explanation for the observed matter-antimatter asymmetry of the universe. KamLAND-Zen is a leading search for 0๐๐ฝ๐ฝ, having placed the most stringent limit on its half-life at [formula] at 90% C.L. in ยนยณโถXe. The next phase of KamLAND-Zen is currently running and will place even more stringent limits on the half-life. The sensitivity of KamLAND-Zen is primarily limited by backgrounds, including the muon spallation background ยนโฐC. We present a machine learning algorithm based on a convolutional neural network (CNN) that is able to separate ยนโฐC events from 136Xe events in Monte Carlo simulated data. With a typical kiloton-scale detector configuration like the KamLAND-Zen detector, we find that the algorithm is capable of identifying 61.6% of the ยนโฐC at 90% signal acceptance. The algorithm is independent of vertex and energy reconstruction, so it is complementary to current methods and can be expanded to other background sources.
MIT Department
Massachusetts Institute of Technology. Department of Physics
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