Convolutional Neural Networks for Signal Reconstruction in High-Energy Calorimetry

Particle accelerators are complex facilities that collide particles at extreme high speed, aiming to discover new physics. In this context, high-energy calorimeter systems play a crucial role, as they provide the particle energy quantity, which is important information for the potential new discoveries. Therefore, this work evaluates the performance of the commonly used Optimal Filter (OF) method and several Convolutional Neural Network (CNN) architectures in reconstructing the amplitude and phase of simulated signals that represent the response pulses produced by high-energy calorimeters. The comparison is conducted using quantitative metrics—including RMS, MAE, MedAE, and Coefficient of Determination. The results show that different CNN architectures exhibit varying performances depending on the calorimeter cell occupancy rate but generally outperform the typical linear OF method, providing more accurate signal reconstructions. Considering all evaluated occupancy levels (10%, 50%, 80%, and 100%), the CNN-based approaches achieved an average improvement of approximately 79% in amplitude RMS and 62% in amplitude standard deviation when compared to the OF method. For phase estimation, the CNNs achieved improvements of approximately 26% for both RMS and standard deviation metrics. Although the proposed strategy requires a large execution time due to the training process across multiple folds, these findings indicate that CNNs are promising alternatives for calorimeter energy reconstruction, particularly in high-occupancy conditions such as those expected for high-luminosity experiments.