Development of Machine Learning-Assisted Spectra Analyzer for the NEWCUT Muon Spectrometer

  • László Oláh International Virtual Muography Institute (Global); Earthquake Research Institute, The University of Tokyo, Tokyo 113-0032, Japan
  • Hiroyuki K. M. Tanaka International Virtual Muography Institute (Global); Earthquake Research Institute, The University of Tokyo, Tokyo 113-0032, Japan
  • Hiroshi Suenaga International Virtual Muography Institute (Global); Central Research Institute of Electric Power Industry (CRIEPI), Chiba 270-1194, Japan
  • Shinichi Miyamoto International Virtual Muography Institute (Global); NEC Corporation (Global)
  • Gábor Galgóczi International Virtual Muography Institute (Global); Wigner Research Centre for Physics, Eotvos Lorand Research Network, Budapest 1121, Hungary
  • Gergő Hamar International Virtual Muography Institute (Global); Wigner Research Centre for Physics, Eotvos Lorand Research Network, Budapest 1121, Hungary
  • Dezső Varga International Virtual Muography Institute (Global); Wigner Research Centre for Physics, Eotvos Lorand Research Network, Budapest 1121, Hungary
DOI: https://doi.org/10.31526/jais.2022.264
Keywords: muography, muon spectra, tracking detector, Monte Carlo simulation, GEANT4, machine learning

Abstract

The precise measurement of low-energy muon spectra is required to improve muography of small-sized objects. The NEWCUT spectrometer is a 6-meter-length, rotatable, Multiwire Proportional-Chamber-based spectrometer that is developed for the measurement of the low-energy spectra of cosmic-ray muons up to a few GeV between vertical and horizontal directions. The spectrometer consists of a consecutive series of nineteen detectors with a positional resolution of approx. 3.5 mm each and lead plates. A neural network-assisted muon spectra analyzer was implemented and trained using GEANT4 simulated data. The energydependent relative differences between the simulated and reconstructed muons suggest that the actual
experimental arrangement can accurately measure the spectra up to an energy of 6 GeV.

Published
2022-04-27
How to Cite
[1]
L. Oláh, “Development of Machine Learning-Assisted Spectra Analyzer for the NEWCUT Muon Spectrometer”, Journal of Advanced Instrumentation in Science, vol. 2022, Apr. 2022.
Issue
Volume 2022
Section
International Workshop on Cosmic-Ray Muography (Muography2021), 24-26 Nov. 2021,
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