  The need for large-scale production of highly accurate simulated event
samples for the extensive physics programme of the ATLAS experiment at the
Large Hadron Collider motivates the development of new simulation techniques.
Building on the recent success of deep learning algorithms, variational
autoencoders and generative adversarial networks are investigated for modelling
the response of the central region of the ATLAS electromagnetic calorimeter to
photons of various energies. The properties of synthesised showers are compared
with showers from a full detector simulation using GEANT4. Both variational
autoencoders and generative adversarial networks are capable of quickly
simulating electromagnetic showers with correct total energies and
stochasticity, though the modelling of some shower shape distributions requires
more refinement. This feasibility study demonstrates the potential of using
such algorithms for ATLAS fast calorimeter simulation in the future and shows a
possible way to complement current simulation techniques.
