npsm 새물리 New Physics : Sae Mulli

pISSN 0374-4914 eISSN 2289-0041
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Article

Research Paper

New Phys.: Sae Mulli 2019; 69: 813-817

Published online August 30, 2019 https://doi.org/10.3938/NPSM.69.813

Copyright © New Physics: Sae Mulli.

Research on Reactor Neutrino Event Selection by Using the Machine Learning Technique

Chang Dong SHIN1, Kyung Kwang JOO*1, Dong Ho MOON1, Myoung Youl PAC2, Junghwan GOH3

1Institute for Universe & Elementary Particles, Department of Physics, Chonnam National University, Gwangju 61186, Korea

2Institute of High Energy Physics, Dongshin University, Naju 58245, Korea
3Department of Physics, Kyung Hee University, Seoul 02447, Korea

Correspondence to:kkjoo@chonnam.ac.kr

Received: June 12, 2019; Revised: July 1, 2019; Accepted: July 2, 2019

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

For the next-generation massive neutrino experiments, selecting a signal in the background events is very important. To do this, we investigated the results of applying a machine learning technique to the selection of neutrino signals. The neutrino signal after inverse beta decay and the background events in a gadolinium-loaded liquid scintillation detector were reproduced by using Monte Carlo simulations. The inverse beta decay process is well-known and has relatively high statistical quantities for this simulation. In this study, an efficiency of signal selection through machine learning was obtained, and in this paper several results are briefly described. Finally, the machine learning technique is expected to become an important tool for use in the next-generation neutrino experiment.

Keywords: Machine learning, Neutrino, Liquid scintillator, Neutrino mixing angle, Next generation neutrino detector

Figures

Fig. 1. Background rejection (a) and signal efficiency (b) by machine learning. The red line is the result using all variables and the green line is the result using only $\Delta T$ and $\Delta R$ without energy variables. Both results show that background can be removed efficiently by machine learning.

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