Classification of the accidental background of delayed neutron captured on hydrogen using machine learning algorithm

Neutrinos are captured on free protons through inverse beta decay (IBD). Among the backgrounds of IBD events, the accidental background is dominant in hydrogen analysis(delayed neutrons are captured on hydrogen : n-H). The accidental background is due to the random association of two signal-like eve...

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Bibliographic Details
Published inJournal of the Korean Physical Society Vol. 80; no. 2; pp. 130 - 134
Main Authors Kwon, Eunhyang, Seo, Ji-Woong, Lee, Hyungi
Format Journal Article
LanguageEnglish
Published Seoul The Korean Physical Society 2022
Springer Nature B.V
한국물리학회
Subjects
Algorithms
Beta decay
Classification
Gadolinium
Hydrogen
Machine learning
Mathematical and Computational Physics
Neutrinos
Original Paper - Particles and Nuclei
Particle and Nuclear Physics
Physics
Physics and Astronomy
Theoretical
물리학
Decision Tree
Machine Learning
Accidental background
Neutrino
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Summary:Neutrinos are captured on free protons through inverse beta decay (IBD). Among the backgrounds of IBD events, the accidental background is dominant in hydrogen analysis(delayed neutrons are captured on hydrogen : n-H). The accidental background is due to the random association of two signal-like events. Due to the low energy criteria of delayed signals, the random backgrounds remain larger than those captured on Gadolinium (Gd). In this study, we classified the accidental random background from IBD candidate events using a machine learning algorithm and compared the results with RENO n-H’s for the classification degree of the backgrounds.
ISSN:0374-4884
1976-8524
DOI:10.1007/s40042-021-00370-1