Geometry-aware Autoregressive Models for Calorimeter Shower Simulations

Understand

Calorimeter shower simulations are often the bottleneck in simulation time for particle physics detectors.

• A lot of effort is currently spent on optimizing generative architectures for specific detector geometries, which generalize poorly.
• We develop a geometry-aware autoregressive model on a range of calorimeter geometries such that the model learns to adapt its energy deposition depending on the size and position of the cells.
• This is a key proof-of-concept step towards building a model that can generalize to new unseen calorimeter geometries with little to no additional training.