CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds

Understand

Precision measurements and new physics searches at the Large Hadron Collider require efficient simulations of particle propagation and interactions within the detectors.

• The most computationally expensive simulations involve calorimeter showers.
• Advances in deep generative modelling - particularly in the realm of high-dimensional data - have opened the possibility of generating realistic calorimeter showers orders of magnitude more quickly than physics-based simulation.
• However, the high-dimensional representation of showers belies the relative simplicity and structure of the underlying physical laws.