Position resolution study at high energies of a sampling electromagnetic calorimeter whose active material is a scintillator with Peroxide-cured polysiloxane base

• Published in: Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment Accelerators, spectrometers, detectors and associated equipment, 2020-03, Vol.955, p.163341, Article 163341
• Main Authors: Aydın, Güral, Sarıgül, Mehmet, Sarıgül, Hasan
• Format: Article
• Language: English
• Subjects: Deep neural network; Geant4; Iterative weighting method; Logarithmic weight; Sampling calorimeter
• ISSN: 0168-9002; 1872-9576
• DOI: 10.1016/j.nima.2019.163341

This study is based on the simulation for the position resolution performances of a sampling electromagnetic calorimeter with a Peroxide-cured polysiloxane based scintillator as an active material. Various algorithms and corrections were applied to reconstruct hit positions. Energy deposition in the center tower and neighboring towers were used to reconstruct the beam impact position in a detector tower module consisted of a 3 x 3 array matrix. Linear weights, corrected linear weights, logarithmic weights, and corrected logarithmic weights were the different algorithms to reconstruct beam hit positions through energy weighted tower positions. Moreover, the iterative weighting method based on the logarithmic weights were applied. The re-weighting algorithm based on the iterative weighting method was seen to improve the results necessarily at relatively low beam energies. Additionally, deep neural network structures were applied over the linear weights without using any logarithmic weights or correction to improve the position resolutions.