Identificação online de sinais baseada em calorimetria de altas energias e fina segmentação

The ATLAS experiment operates in the environment of the large hadron collider, the LHC, located at CERN, Switzerland. LHC accelerates bunches of protons in opposite directions and collide them in specific collision points every 25 ns, reaching up to 14 TeV of energy at the center of mass. Through these collisions, it is possible to probe deep into matter and explore interesting physical process in a way which was never seen by science. As much of the produced information is related to uninteresting physics, it was implemented an online trigger system to select possible events from the interesting physical channels produced by LHC. In particular, the first level trigger uses compact information from both calorimeters and muon chambers for a fast event selection. The other high-level triggers access the full detector resolution, yielding high performance algorithms for particle identification. The muon chambers, though, can be significantly degraded by the strong radioactive environment at the cavern where ATLAS rests. On the other hand, the high-level triggers are submitted to an enormous particle background, which challenges the particle identification. In both cases, calorimetry information plays a central role. This thesis combines calorimetry information, signal processing and pattern recognition techniques, in order to improve the data acquisition bandwidth, allowing more interesting events to be recorded by the ATLAS online trigger system.