Nonlinear Correction for an Energy Estimator Operating at Severe Pile-Up Conditions

For systems operating at high event rates, the readout signal may be distorted by the presence of information from adjacent events. The signal superposition, or pile-up, degrades the efficiency of linear methods, which are typically used for signal parameter estimation. In many applications , the estimation task reduces to determine the amplitude of the incoming signal. In the context of high-energy calorimeters, which aim at measuring the energy of high-energy subproducts of interactions, the signal energy is measured by estimating the amplitude of the received digitized pulse. Modern particle colliders may operate at an event rate much higher than their calorimeter time response length and, as a result, the signal pile-up may be observed. This chapter describes how a computational intelligence approach can assist on the energy estimation performed by an optimal linear method. An artificial neural network is trained aiming at correcting for the nonlinearities introduced by the signal pile-up statistics. The efficiency of the various energy estimation methods is evaluated from simulation data under various signal pile-up scenarios.