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Muon energy estimate through multiple scattering with the MACRO detector
Journal article   Open access   Peer reviewed

Muon energy estimate through multiple scattering with the MACRO detector

M. Ambrosio, R. Antolini, G. Auriemma, D. Bakari, A. Baldini, G.C. Barbarino, B.C. Barish, G. Battistoni, Y. Becherini, R. Bellotti, …
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment, v 492(3), pp 376-386
2002
url
https://doi.org/10.1016/s0168-9002(02)01413-4View
Published, Version of Record (VoR)Open Access (License Unspecified) Open

Abstract

Multiple scattering Muons Neural network Neutrino oscillation
Muon energy measurement represents an important issue for any experiment addressing neutrino-induced up-going muon studies. Since the neutrino oscillation probability depends on the neutrino energy, a measurement of the muon energy adds an important piece of information concerning the neutrino system. We show in this paper how the MACRO limited streamer tube system can be operated in drift mode by using the TDCs included in the QTPs, an electronics designed for magnetic monopole search. An improvement of the space resolution is obtained, through an analysis of the multiple scattering of muon tracks as they pass through our detector. This information can be used further to obtain an estimate of the energy of muons crossing the detector. Here we present the results of two dedicated tests, performed at CERN PS-T9 and SPS-X7 beam lines, to provide a full check of the electronics and to exploit the feasibility of such a multiple scattering analysis. We show that by using a neural network approach, we are able to reconstruct the muon energy for E μ<40 GeV . The test beam data provide an absolute energy calibration, which allows us to apply this method to MACRO data.

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Instruments & Instrumentation
Nuclear Science & Technology
Physics, Nuclear
Physics, Particles & Fields
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