Presenter: Luc Lisi
Faculty Mentor: Stephanie Majewski
Presentation Type: Poster 25
Primary Research Area: Science
Major: Physics
The Large Hadron Collider (LHC) is a proton-proton particle collider that at the present (2016) is the most powerful particle accelerator in the world. At peak operation, there can be as many as 600 million proton-proton collisions
per second and as a result, deciding which events are useful to analysis and which events are not, in real time, is paramount to data collection. To accomplish this, accurate calorimeter object reconstruction and suppression of multiple interactions per bunch crossing (pileup) in the ATLAS detector at the Large Hadron Collider plays a key role in triggering on important proton-proton collision events. In particular, we aim to improve the performance of the jet and missing transverse energy triggers. We present simulation studies of a novel algorithm for the Level-1 Calorimeter trigger in the Phase-I and Phase-II upgrades of the trigger electronics that aims to improve this trigger efficiency. Inspired by image processing techniques, we use gradient estimation to extract areas of topological interest in the 0.2×0.2 (in eta-phi) towers of the global feature extractor (gFEX), a component of the Level-1 trigger system for the Phase-I upgrade. Our preliminary results have found that these techniques are capable of suppressing pileup and reconstructing calorimeter objects in simulated events. However, further studies must be conducted to understand the algorithm’s speed, efficiency, and other factors critical to implementation in the final trigger.