Fast online filtering based on data fusion of two highly segmented detectors

Abstract

The Tile Calorimeter (TileCal) is the central hadronic calorimeter of the ATLAS experiment at the Large Hadron Collider (LHC). TileCal provides highly-segmented energy measurements for incident particles. Information from TileCal’s outermost radial layer in coincidence with the ATLAS muon chambers can provide a reduction of the fake muon triggers due to slow charged particles (typically protons). The TileMuon project was development aims this purpose and its main activity of the Tile-Muon Trigger in the ATLAS upgrade program is to install and to activate the TileCal signal processor module for providing trigger inputs to the Level-1 Muon Trigger. This dissertation presents the study, the development and the implementation of the Muon identification techniqueinthe TileMuon context. Amplitude estimation techniques found in the literature were applied to the problem and confronted against each other. The results for experimental data shown that the Muon identification based on the maximum likelihood for Gaussian noise achieved the best performance in terms of detection error as well as online implementation feasibility, and it has been the chosen technique for the application.