Sparse Representation for Signal Reconstruction in Calorimeters Operating in High Luminosity

Abstract

A calorimeter signal reconstruction method, based on sparse representation (SR) of redundant data, is proposed for energy reconstruction in particle colliders operating in high-luminosity conditions. The signal overlapping is first modeled as an underdetermined linear system, leading to a convex set of feasible solutions. The solution with the smallest number of superimposed signals (the SR) that represents the recorded data is obtained through the use of an interior-point (IP) optimization procedure. From a signal processing point-of-view, the procedure performs a source separation, where the information of the amplitude of each convoluted signal is obtained. In the simulation results, a comparison of the proposed method with standard signal reconstruction one was performed. For this, a toy Monte Carlo simulation was developed, focusing in calorimeter front-end signal generation only, where the different levels of pileup and signal-to-noise ratio were used to qualify the proposed method. The results show that the method may be competitive in high-luminosity environments.