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.

Keywords

• unfolding momentum spectrum
• muon beam
• multiple coulomb scattering
• deep neural network

Çoklu Coulomb Saçılma Verileri ile Derin Sinir Ağlarını Kullanarak Müon Enerjisinin Tahmin Edilmesi

Abstract

Bu çalışma, yapay sinir ağlarında çoklu Coulomb saçılma verileri kullanılarak müon ışını enerjilerinin belirlenmesine dayanmaktadır. Müon parçacıkları, Geant4 tabanlı G4beamline benzetim programı kullanılarak 50 katmanlı bir kurşun nesneden saçıldı. Derin sinir ağları ile çalışmadan önce, katman sayısı cinsinden ortalama saçılma açısı dağılımları, müon ışını enerjilerini tahmin etmek için çoklu Coulomb saçılımı için iyi bilinen formül kullanılarak fit yöntemiyle analiz edildi. Daha sonra, müon ışını enerjisini tahmin etmek için derin sinir ağı yapılarında 1'den 10'a kadar katman sayısı üzerinden ortalama saçılma açıları kullanıldı. Derin sinir ağlarının, fit yöntemine göre çözünürlükleri önemli ölçüde iyileştirdiği gözlemlenmiştir.

Keywords

• momentum spektrumu çözülmesi
• müon ışını
• çoklu coulomb saçılması
• derin sinir ağları

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