  In particle physics the simulation of particle transport through detectors
requires an enormous amount of computational resources, utilizing more than 50%
of the resources of the CERN Worldwide Large Hadron Collider Grid. This
challenge has motivated the investigation of different, faster approaches for
replacing the standard Monte Carlo simulations. Deep Learning Generative
Adversarial Networks are among the most promising alternatives. Previous
studies showed that they achieve the necessary level of accuracy while
decreasing the simulation time by orders of magnitudes. In this paper we
present a newly developed neural network architecture which reproduces a
three-dimensional problem employing 2D convolutional layers and we compare its
performance with an earlier architecture consisting of 3D convolutional layers.
The performance evaluation relies on direct comparison to Monte Carlo
simulations, in terms of different physics quantities usually employed to
quantify the detector response. We prove that our new neural network
architecture reaches a higher level of accuracy with respect to the 3D
convolutional GAN while reducing the necessary computational resources.
Calorimeters are among the most expensive detectors in terms of simulation
time. Therefore we focus our study on an electromagnetic calorimeter prototype
with a regular highly granular geometry, as an example of future calorimeters.
