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Background rejection with neural network for KamLAND-Zen
2020
Zenodo
KamLAND-Zen is a neutrinoless double beta decay $(0\nu\beta\beta)$ search experiment using $^{136}$Xe. Taking advantages of the low-background environment of KamLAND, we realize the most sensitive $0\nu\beta\beta$ search. While $0\nu\beta\beta$ is a pure $\beta$ event, the backgrounds such as $^{214}$Bi and spallation products emit $\gamma$-rays. Therefore, particle identification(PID) is effective to improve the sensitivity. In this study, we develop a PID method with a neural network focusing
doi:10.5281/zenodo.4122868
fatcat:r77dowyth5ftxev4zi65wcqowu