Speaker
Description
Accurate simulation of the interaction of particles with the detector materials is of
utmost importance for the success of modern particle physics. Software libraries like
GEANT4 are tools that already allow the modeling of physical processes inside detectors
with high precision. The downside of this method is its computational cost in terms of
time.
Recent developments in generative machine learning models seem to provide a promising
alternative for faster and accurate simulations to accelerate this process. We show the
taken steps in the development of a GraphGAN for the simulation of the CMS High
Granularity Calorimeter (HGCal) that is being developed for the High-Luminosity upgrade
at the LHC.
As a first result, we will show an energy regression using Graph Neural Networks.
