Energy Estimation Considerations for a High-Energy Calorimeter Operating at High Pile-Up Conditions

Abstract

The high-luminosity LHC program introduces new challenges to calorimeter energy reconstruction. Due to the increased number of interactions per collision, the calorimeter readout signal may be distorted by pile-up effects, which introduce nonlinearities into the readout channel noise and degrade the performance of standard energy estimation algorithms. This work evaluates the performance of two promising methods recently proposed to address the pile-up effect in high-energy calorimetry. The first method is based on a signal deconvolution technique, while the second combines a linear approach with a neural network that refines the linear estimation. Both methods are developed for the ATLAS Tile Calorimeter, currently in operation at the LHC. A simulation dataset was generated covering a wide range of pile-up scenarios expected for the high-luminosity LHC. The results show that energy estimation efficiency can be significantly improved.