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The ATLAS Trigger Performance and Evolution

During the data taking period from 2009 until 2012, the ATLAS trigger has been very successfully used to collect proton-proton data at LHC centre-of-mass energies between 900 GeV and 8 TeV at record breaking luminosities. The three‐level trigger system reduces the event rate from the design bunch‐cr...

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Detalles Bibliográficos
Autor principal: Bartsch, V
Lenguaje:eng
Publicado: 2012
Materias:
Acceso en línea:http://cds.cern.ch/record/1490039
Descripción
Sumario:During the data taking period from 2009 until 2012, the ATLAS trigger has been very successfully used to collect proton-proton data at LHC centre-of-mass energies between 900 GeV and 8 TeV at record breaking luminosities. The three‐level trigger system reduces the event rate from the design bunch‐crossing rate of 40 MHz to an average recording rate of about 300 Hz. Using custom electronics with input from the calorimeter and muon detectors, the first level rejects most background collisions in less than 2.5 μs. Then follow two levels of software‐based triggers. The trigger system is designed to select events by identifying muons, electrons, photons, taus, jets, and B hadron candidates, as well as using global event signatures, such as missing transverse energy. We give an overview of the strategy and performance of the different trigger selections during the 2011-2012 run. We also discuss the trigger evolution and redesign put in place to cope with the continuously rising luminosity and in particular the challenges of processing the higher than design collision rate per bunch crossing experienced during the 2012 run. Distributions of selection variables used by the different trigger selection are shown and compared with the offline reconstruction. Examples of trigger efficiencies with respect to offline reconstructed signals are presented and compared to simulation and as a function of luminosity. These results illustrate that we have achieved a very good level of understanding of both the detector and trigger performance and successfully selected suitable streamed data samples for analysis.