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

Aydin, Güral2024-09-192024-09-1920222148-3736https://doi.org/10.31202/ecjse.1017848https://hdl.handle.net/20.500.12483/14905This study is based on determining 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 regarding the number of crossed layers were analyzed with the fit 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 fit method. © 2022, TUBITAK. All rights reserved.eninfo:eu-repo/semantics/openAccessdeep neural networkmultiple Coulomb scatteringmuon beamUnfolding momentum spectrumPrediction of Muon Energy using Deep Neural Network with Multiple Coulomb Scattering DataÇoklu Coulomb Saçılma Verileri ile Derin Sinir Ağlarını Kullanarak Müon Enerjisinin Tahmin EdilmesiArticle9397598710.31202/ecjse.10178482-s2.0-85139753168Q4