Speakers
Description
One current focus of calorimeter reconstruction algorithm development has been the utilisation of time information as the fifth dimension of calorimeter data.
In this talk, recent work of the CALO5D team is presented, studying the role of timing in highly granular CALICE-type calorimeters. The studies focus on the calorimeter concept as implemented in the International Large Detector (ILD) concept proposed for the ILC and FCC-ee.
The talk will cover the ongoing re-optimisation of the ILD electromagnetic calorimeter (ECal), including the interplay between detector design, timing performance and reconstruction. Particular emphasis will be placed on machine-learning-based reconstruction approaches and their performance compared with established particle-flow (PFlow) reconstruction such as Pandora and APRIL. These approaches are investigated across different levels of complexity, ranging from the optimisation of specific observables, such as the electromagnetic energy fraction f_em, to fully end-to-end reconstruction of calorimeter data. Their impact on jet energy resolution and other relevant reconstruction observables will be discussed.
In addition, the implications of operating such a highly granular calorimeter at the FCC-ee are considered. In particular, studies of the expected data rates and power consumption will be presented, addressing the challenges posed by the large volume of highly granular, time-resolved calorimeter data and their implications for the design and operation of a future detector. Finally, the implementation of the CALICE-type calorimeter in the simulation framework of the SHiP experiment and its possible impact on the detector performance are presented.