Deep Learning at TRIUMF

Summary Energy reconstruction in the ATLAS calorimeter is the practice of associating particle energy deposits in the calorimeter with the particle that caused them. This is a crucial step in identifying particles and reconstructing the event that produced them. Current methods to perform reconstruction rely on algorithms that grow with quadratic complexity, posing a challenge for the High-Luminosity LHC (HL-LHC) upgrade, which promises a significant increase in the number of collisions per event and, therefore, the complexity of each event. [Read More]