Performance Evaluation of Energy Reconstruction Methods in High Energy Physics Experiments

Abstract

The discovery of particles that shape our universe drives the scientific community to develop increasingly sophisticated equipment. Particle accelerators are complex machines that bring particle beams into collision at speeds close to the speed of light. When these collisions occur, secondary products are generated and measured by calorimeter systems, which fully absorb them. Typically, high-energy calorimeters are highly segmented, comprising thousands of dedicated readout channels. This work evaluates the performance of two energy reconstruction algorithms: the Optimal Filter (OF) and the Multi-Amplitude Estimator (MAE), recently proposed to address signal superposition (pile-up). To assess energy estimation efficiency, artificial data are used under various pile-up conditions. Statistical analysis of the estimated energy is then employed to compare the performance of each method. A second analysis evaluates the sensitivity of the MAE to the pedestal parameter. The results show that the MAE method outperforms the OF method, although the use of an uncalibrated pedestal value negatively affects its performance.