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

Samuel, D.; Samalan, A.; Omana Kuttan, M.; Murgod, L.P.

doi:10.1088/1748-0221/14/11/P11020

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

D. Samuel
(
- Unlisted, IN and
- Saha Inst.
)
,
A. Samalan
(
- Unlisted, IN and
- Gent U.
)
,
M. Omana Kuttan
(
- Unlisted, IN and
- Frankfurt U., FIAS
)
,
L.P. Murgod
(
- Unlisted, IN
)

Nov 18, 2019

12 pages

Published in:

JINST 14 (2019) 11, P11020

Published: Nov 18, 2019

DOI:

10.1088/1748-0221/14/11/P11020

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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

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