Speaker
Description
The ATLAS trigger system will undergo a comprehensive upgrade in advance of the HL-LHC programme. In order to deal with the increased data bandwidth trigger algorithms will be required to satisfy stricter latency requirements. We propose a method to speed up the current calorimeter-only preselection step and to aid trigger decisions for hadronic signals containing jets.
We demonstrate the use of a dedicated object-detection Convolutional Neural Network (CNN) for jet finding in the ATLAS calorimeter. The modified computer vision model is employed in the task of jet detection to identify and localise jets within the central calorimeter acceptance and to subsequently estimate their transverse momenta. A custom architecture is introduced to reduce the number of learnable parameters required for improved inference speed. The model performance is evaluated on a set of simulated particle interactions in the ATLAS detector with up to 200 concurrent pile-up interactions.