Análise do desempenho de modelos lineares para a reconstrução de sinal do calorímetro de telhas do experimento ATLAS no contexto do HL-LHC

Abstract

The technological advancement of computing has significantly boosted the field of signal processing. One of the central topics in this area, signal reconstruction, presents cutting-edge applications in high-energy particle physics. Atomic collisions generate large amounts of data in short time intervals. Thus, there is a need for sophisticated detectors to record the events of interest in various reading channels with precision and accuracy. With the advent of the High Luminosity Large Hadron Collider (HL-LHC), a substantial increase in collision rate is expected, with greater luminosity, parameter directly related to the density of the beams. Although this high luminosity scenario amplifies the potential for scientific exploration, it also intensifies the stacking of signals in a same reading channel, making the reconstruction process difficult. This work analyzes linear methods for estimation of fundamental parameters to the reconstruction of simulated signals in the context of the HL-LHC, using as reference the scintillating tile calorimeter (TileCal) from the ATLAS experiment. The methods Best Linear Unbiased Estimator (BLUE), Optimal Filter (OF), Least Squares (LS), Wiener Filter (WF) and Signal Deconvolution (SD) were proposed, with emphasis on estimating the amplitude and phase response of the TileCal channel. In addition, the impact of sample window on estimation error was investigated, considering the sequence of data provided at each collision moment. The results indicate that methods using reference parameters, such as LS and WF, have superior performance in several operational scenarios of the HL-LHC.