Estimação de energia para calorimetria em física de altas energias baseada em representação esparsa

Abstract

This thesis proposes a new approach based on sparse representation for the energy estimation problem in high energy calorimetry operating in pile-up scenarios. This work was mainly motivated by the progressive increase of the LHC luminosity and its consequences on the energy estimation problem for channels of the electromagnetic calorimeter of ATLAS (LArg), in the context of the ATLAS experiment upgrade program at the LHC. Two estimation methods were proposed and named SPARSE and SPARSE-COF, both using linear programming in the search for sparsity. These methods were evaluated in several simulations and compared with the classical method used in ATLAS calorimeters, called OF, and with DM-COF, a recently developed method for the ATLAS hadronic calorimeter that addresses pileup problem in its formulation. In the various simulations performed, SPARSE and SPARSE-COF methods performed better than others, especially when the observation window used for energy estimation does not contain all samples of the typical calorimeter pulse, operating in pile-up scenarios. In addition, through LArg Monte Carlo simulations, the methods based on sparse representation were evaluated using linear programming and also sparse methods with less computational complexity, such as IRLS, OMP and LS-OMP. The results showed that the LS-OMP method presented performance equivalent to the SPARSE and SPARSE-COF methods,qualifying it as a candidate to be used for online energy estimation in LArg.