Edge detection and Deep Learning Algorithm Performance Studies for the ATLAS Trigger System.

The upcoming ATLAS Phase-I upgrade at the Large Hadron Collider (LHC) planned for 2019-2020 will incorporate the Global Feature Extractor (gFEX), a component of the Level-1 Calorimeter trigger that is intended for the detection and selection of energy coming from hadronic decays emerging from proton-proton collisions at √13TeV in the ATLAS detector. As the luminosity at the LHC increases, the acquisition of data in the ATLAS trigger system becomes very challenging and rejecting background events in this high pileup environment (up to 80 interactions per bunch crossing) will require more sophisticated techniques. To achieve this goal, edge-detection and deep learning techniques that could be adapted for the gFEX’s Field Programable Gate Array (FPGA) architecture are being investigated. The focus of this study is to analyze the performance of these algorithms using Monte Carlo simulations of Lorentz-boosted top quark decays in order to increase the efficiency of signal detection given a fixed background rejection in our trigger system.