Presenter(s): Adrian Gutierrez
Faculty Mentor(s): Stephanie Majewski
Oral Session 2 C
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 the LHC. As the luminosity at the LHC increases, the acquisition of data in the ATLAS trigger system becomes very challenging and rejecting background events in high pileup environment (up to 80 interactions per bunch crossing) is necessary. 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. Comparing the results of the edge detection and deep learning algorithms has shown an improvement in our trigger system efficiency that exploits the capabilities of the gFEX and could potentially be implemented to help us detect particles that are not described by our current theories.