A peer-reviewed journal published by K. N. Toosi University of Technology

Imbalanced classification of antineutrino and cosmic muon in segmented plastic scintillator antineutrino detector

Document Type : Research Article

Authors

1 Department of Physics‎, ‎K.N‎. ‎Toosi University of Technology‎, ‎Tehran‎, ‎Iran

2 Department of Physics‎, ‎University of Bojnord‎, ‎Bojnord‎, ‎Iran

Abstract
Inverse beta decay (IBD) in plastic scintillators is one of the most commonly used methods for detecting reactor antineutrinos. Cosmic muon signals due to the IBD compared to those generated by antineutrinos are still the main challenge in these types of detectors. The IRAND (IRan ANtineutrino Detector) is currently being designed and implemented with the constraint of reducing the required hardware, and at the same time, improving the antineutrino detection efficiency. Imbalanced classification is one of the software methods in machine learning that deals with imbalanced data, such as muon and antineutrino. Using the IRAND-Sim simulation package based on the Geant4 toolkit presented in our previous research, the spectra and angular distribution of antineutrinos and muons can be calculated. However, in this study, the memory management techniques to handle the dataset due to a large number of muons have been used, and also two separate methods have been used in the imbalanced classification for discriminating muon and antineutrino events. The results show that this approach by combining real and simulated data is very efficient, and the imbalanced nature can be reduced to achieve better classifier performance.

Highlights

  • Inverse beta decay in IRan ANtineutrino Detector as segmented plastic scintillators for antineutrino detection.
  • IRAND-Sim Simulation Package for spectra and angular distribution of antineutrinos and muons.
  • Memory management techniques to handle the dataset due to the large number of muons.
  • Two methods of imbalanced classification for discriminating muon and antineutrino events.

Keywords


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Volume 5, Issue 3
Summer 2024
Pages 25-34

  • Receive Date 05 May 2024
  • Revise Date 01 June 2024
  • Accept Date 01 June 2024