Speaker
Description
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 on difference of scintillation timing property between $\beta$ and $\gamma$.
It rejects gamma backgrounds based on hit-timing spectrum of PMTs.
We applied the method to hit-timing spectrum of MC and data of KamLAND-Zen400 Phase1, where $^{110m}$Ag (gamma event) was the most dominant background. We found that this method could reject 60% of gamma backgrounds and had a potential of ~10% improvement of its limit.
Mini-abstract
Particle identification with neural networks can reduce the backgrounds of KamLAND-Zen experiment
| Experiment/Collaboration | KamLAND-Zen |
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