Speaker
Walker Bennett Hendricks
(Baylor University (US))
Description
In the approaching post-LHC era, it is clear that current calorimeter technologies need improvements in energy resolution to achieve the physics goals of a Higgs factory. Dual-readout calorimetry does this by comparing signals of both scintillation and Cherenkov light to more accurately determine the electromagnetic fraction in a hadronic shower. To this end, the CalVision group has created a GEANT4-based simulation to predict experimental results from a crystal electromagnetic calorimeter. This report describes the creation of a simulation data set for usage in physics studies of dual-readout calorimetry. The specific application of this data to the training of neural networks for implementation in on-detector ASICs is also outlined.
Author
Walker Bennett Hendricks
(Baylor University (US))