Speaker
Description
In modern particle detectors, calorimeters provide critical energy measurements of particles produced in high-energy collisions. The demanding requirements of next-generation collider experiments would benefit from a systematic approach to the optimization of calorimeter designs. The performance of calorimeters is primarily characterized by their energy resolution, parameterized by a stochastic term which reflects sampling fluctuations, and a constant term, accounting for calibration uncertainties and non-uniformities. These terms serve as figures of merit for detector performance, leading to improved reconstruction of physics objects.
This work focuses on optimizing the layer composition of hadronic calorimeters for the FCC detector concepts. Through detailed GEANT4-based simulations, and the use of lightweight full detector simulation tools (COCOA), we analyze the impact of varying passive and active material proportions and layer thickness distribution on energy resolution performance. Our methodology aims to isolate these contributions from other design factors, in order to develop a closed optimization framework that evaluates configurations against physics performance targets, while still addressing practical constraints.
