Machine learning-based predictions of directionality and charge of cosmic muons—a simulation study using the mICAL detector

Nov 18, 2019
12 pages
Published in:
  • JINST 14 (2019) 11, P11020
  • Published: Nov 18, 2019

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Abstract: (IOP)
The Iron Calorimeter (ICAL) is a neutrino physics experiment proposed by the India-based Neutrino Observatory (INO) collaboration to measure the oscillation parameters. The mini Iron Calorimeter (mICAL) detector is a small-scale prototype of ICAL built at the Inter-Institutional Centre for High Energy Physics (IICHEP), Madurai, India. In this paper, we present the simulation study of machine learning-based predictions of directionality and charge of cosmic muons using the mICAL detector geometry.
  • Analysis and statistical methods
  • Pattern recognition
  • cluster finding
  • calibration and fitting methods
  • Simulation methods and programs
  • calorimeter: iron
  • India based Neutrino Observatory
  • neutrino/mu: particle identification
  • antineutrino/mu: particle identification
  • track data analysis