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Precision measurements and new physics searches at the Large Hadron Colliderrequire efficient simulations of particle propagation and interactions withinthe detectors. The most computationally expensive simulations involvecalorimeter showers. Advances in deep generative modelling - particularly inthe realm of high-dimensional data - have opened the possibility of generatingrealistic calorimeter showers orders of magnitude more quickly thanphysics-based simulation. However, the high-dimensional representation ofshowers belies the relative simplicity and structure of the underlying physicallaws. This phenomenon is yet another example of the manifold hypothesis frommachine learning, which states that high-dimensional data is supported onlow-dimensional manifolds. We thus propose modelling calorimeter showers firstby learning their manifold structure, and then estimating the density of dataacross this manifold. Learning manifold structure reduces the dimensionality ofthe data, which enables fast training and generation when compared withcompeting methods.

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