Angular Conditioning of Deep Generative Models for Fast Simulation of High Granularity Calorimeters

Modern high energy physics crucially relies on simulation to connect experimental observations to underlying theory. While traditional methods relying on Monte Carlo techniques produce powerful simulation tools, they prove to be computationally expensive. This is particularly true when they are applied to calorimeter shower simulation, where many particle interactions occur. The strain on computing resources due to simulation is projected to be so large as to be a major bottleneck at the high luminosity stage of the LHC and for future colliders.Deep generative models have attracted significant attention as an approach which promises to drastically reduce the computing time required for simulation. Recent work in our group has demonstrated the capability of various generative models to accurately reproduce showers displaying key physics properties in a highly granular calorimeter. This initial work focused on the specific case of a particle incident perpendicular to the calorimeter face, however a practical simulator must incorporate arbitrary angles of incidence and simulate them correctly. This talk will address ongoing efforts to add conditioning on the incident angle of the particle. In particular, we demonstrate the crucial importance of modifying an existing loss function via the addition of an auxiliary constrainer network, in order to improve the angular performance of a generator