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A data volume of 60 TB/s is expected from the high LHC collision rate and high resolution of the ATLAS detectors. To cope with this bandwidth, a highly programmable, three-level online triggering system is under development. One of the main components of this system is an electron/jet discriminator that uses the highly segmented calorimetry information. In this work, we address the electron/jet discrimination at the second-level trigger by building a set of concentric ring sums around the energy deposition peak in each calorimeter segment. An Independent Component Analysis (ICA) on these ring sums is then performed to extract the main sources of the calorimeter signal. The extracted independent components feed the input nodes of a neural electron/jet discriminator. The proposed system is able to achieve higher detection efficiency than the current electron/jet discriminating system operating in ATLAS, while being fast enough to cope with the time restrictions of the ATLAS triggering system operation.

Online Electron/Jet Neural High-level Trigger Over Independent Calorimetry Information