Open AccessPERFORMANCE EVALUATION OF ENERGY RECONSTRUCTION METHODS IN HIGH ENERGY PHYSICS EXPERIMENTS
Author(s) -
Juan Lieber Marin,
E. Furtado De Simas Filho,
B. S. Peralva,
Guilherme Inácio Gonçalves,
L. Manhaes de Andrade Filho,
J. M. Seixas
Publication year - 2020
Publication title -
revista mundi engenharia, tecnologia e gestão
Language(s) - English
Resource type - Journals
ISSN - 2525-4782
DOI - 10.21575/25254782rmetg2020vol5n21169
Subject(s) - calorimeter (particle physics) , energy (signal processing) , pedestal , superposition principle , computer science , estimator , filter (signal processing) , sensitivity (control systems) , computational physics , physics , algorithm , optics , simulation , electronic engineering , mechanical engineering , engineering , computer vision , mathematics , statistics , quantum mechanics , detector
The discovery of particles that shape our universe pushes the scientific community to increasingly build sophisticated equipment. Particle accelerators are one of these complex machines that put known particle beams on a collision course at speeds close to that of light. When collisions occur, subproducts are produced and measured by the calorimeter system, which entirely absorbs these subproducts. Typically, a high-energy calorimeter is highly segmented, comprising thousands of dedicated readout channels. The present work evaluates the performance of two energy reconstruction algorithms: the OF (Optimal Filter) and MAE (Multi-Amplitude Estimator), which was recently proposed to deal with the signal superposition (pile-up). In order to evaluate the energy estimation efficiency, artificial data were used, considering several pile-up levels. The statistics from the energy estimation is employed to compare the performance achieved by each method. A second analysis is made to quantify the MAE sensitivity to the pedestal parameter. The results show that the MAE method presents a better performance than the OF method and the usage of an uncalibrated pedestal value compromises the MAE performance.