CALORIMETER BASED VERTEXING FOR THE ATLAS NEXT GENERATION TRIGGER

This report focuses on the creation and implementation of a particle tracking and vertexing technique proposed for the ATLAS Next Generation Global Trigger. Most of the project’s time was spent on developing and testing a linear and an extended Kalman filter algorithm with data from an open dataset. As a next step to that, a modified Kalman filter was developed, combining the estimator capabilities of the extended Kalman filter (EKF) and the usefulness of a novel evolutionary driven ML symbolic regression (SR) method, in order to make a possibly more accurate inference about a particle’s interaction vertex than other contemporary methods. This technique can be later applied to the the ATLAS Global Trigger by utilizing energy depositions from the sampling layers of the ATLAS electromagnetic-liquid argon calorimeter as input (seeds) for the Kalman filter.