Anti-electron Neutrino Event Selection from Backgrounds Based on Machine Learning

For reactor neutrino experiments including the next--generation experiments will be adopting the liquid scintillator technique, criteria and time to select neutrino--induced inverse beta decay events from the background events need to be established. For higher performance efficiency, we investigate...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2019-07
Main Authors: Chang Dong Shin, Joo, Kyung Kwang, Dong Ho Moon, June Ho Choi, Myoung Youl Pac, Goh, Junghwan
Format: Article
Language:English
Subjects:
Artificial intelligence
Beta decay
Computer simulation
Gadolinium
Machine learning
Monte Carlo simulation
Neutrinos
Scintillation counters
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:For reactor neutrino experiments including the next--generation experiments will be adopting the liquid scintillator technique, criteria and time to select neutrino--induced inverse beta decay events from the background events need to be established. For higher performance efficiency, we investigated the results of applying a machine learning technique embedded in a standard ROOT package to select IBD signals. To obtain a higher statistics, the signals and background events in a gadolinium-loaded liquid scintillation detector were reproduced by Monte Carlo simulation. We report the efficiencies of neutrino--induced \(n-H\) and \(n-Gd\) events selection using the machine learning technique.
ISSN:2331-8422