  A method is proposed and evaluated to model large and inconvenient phase
space files used in Monte Carlo simulations by a compact Generative Adversarial
Network (GAN). The GAN is trained based on a phase space dataset to create a
neural network, called Generator (G), allowing G to mimic the multidimensional
data distribution of the phase space. At the end of the training process, G is
stored with about 0.5 million weights, around 10 MB, instead of few GB of the
initial file. Particles are then generated with G to replace the phase space
dataset.
  This concept is applied to beam models from linear accelerators (linacs) and
from brachytherapy seed models. Simulations using particles from the reference
phase space on one hand and those generated by the GAN on the other hand were
compared. 3D distributions of deposited energy obtained from source
distributions generated by the GAN were close to the reference ones, with less
than 1% of voxel-by-voxel relative difference. Sharp parts such as the
brachytherapy emission lines in the energy spectra were not perfectly modeled
by the GAN. Detailed statistical properties and limitations of the
GAN-generated particles still require further investigation, but the proposed
exploratory approach is already promising and paves the way for a wide range of
applications.
