Prediction of Muon Energy using Deep Neural Network with Multiple Coulomb Scattering Data

Abstract

This study is based on the determination of muon beam energies using multiple Coulomb scattering data in artificial neural networks. Muon particles were scattered off a 50-layer lead object by using the G4beamline simulation program which is based on Geant4. Before working with deep neural networks, average scattering angle distributions in terms of the number of crossed layers were analyzed with the fitting method using the well-known formula for multiple Coulomb scattering to estimate muon beam energies. Subsequently, average scattering angles over the number of crossed layers from 1 to 10 were used in deep neural network structures to estimate the muon beam energy. It has been observed that deep neural networks significantly improve the resolutions compared to the ones obtained with the fitting method.