A matched filter based approach for high-energy estimation in calorimetry

Abstract

In high-energy calorimeters, a crucial task is the reconstruction of the energy deposited by particle interactions. Standard techniques used in modern calorimeters rely on energy estimation to select signals with relevant information. This work presents a new approach that performs signal detection in the presence of noise as a first step, followed by energy estimation. The method is fully based on Matched Filter (MF) theory, which is known to provide optimal detection efficiency with respect to the signal-to-noise ratio. Furthermore, the MF output can be calibrated to estimate the signal amplitude and, consequently, the energy. The proposed method is compared with different optimal filtering algorithms currently used for energy reconstruction in modern calorimeter systems. Simulation results show that the proposed approach achieves superior performance in terms of both signal detection efficiency and estimation error.