Speaker
Mr
Yunjae Lee
(University of Seoul, Department of Physics (KR))
Description
Dual-readout calorimeters utilize two distinct readouts from scintillation and Cerenkov fibers to measure energy, yielding high hadronic energy resolution. While these calorimeters can reconstruct the energy, position, and particle type of detected showers, conventional methods are limited to distinguishing between electromagnetic and hadronic particles. To overcome this limitation, we explore deep learning algorithms to optimize particle reconstruction across different regions of the calorimeter and to extend the identification of particle types. This study evaluates of the performance of particle reconstruction using deep learning-based algorithms which is optimized for dual-readout calorimeters.
Authors
Jason Lee
(University of Seoul (KR))
Mr
Yunjae Lee
(University of Seoul, Department of Physics (KR))
