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The need for large-scale production of highly accurate simulated eventsamples for the extensive physics programme of the ATLAS experiment at theLarge Hadron Collider motivates the development of new simulation techniques.Building on the recent success of deep learning algorithms, variationalautoencoders and generative adversarial networks are investigated for modellingthe response of the central region of the ATLAS electromagnetic calorimeter tophotons of various energies. The properties of synthesised showers are comparedwith showers from a full detector simulation using GEANT4. Both variationalautoencoders and generative adversarial networks are capable of quicklysimulating electromagnetic showers with correct total energies andstochasticity, though the modelling of some shower shape distributions requiresmore refinement. This feasibility study demonstrates the potential of usingsuch algorithms for ATLAS fast calorimeter simulation in the future and shows apossible way to complement current simulation techniques.

Deep generative models for fast photon shower simulation in ATLAS