  Highly reliable Monte-Carlo event generators and detector simulation programs
are important for the precision measurement in the high energy physics. Huge
amounts of computing resources are required to produce a sufficient number of
simulated events. Moreover, simulation parameters have to be fine-tuned to
reproduce situations in the high energy particle interactions which is not
trivial in some phase spaces in physics interests. In this paper, we suggest a
new method based on the Wasserstein Generative Adversarial Network (WGAN) that
can learn the probability distribution of the real data. Our method is capable
of event generation at a very short computing time compared to the traditional
MC generators. The trained WGAN is able to reproduce the shape of the real data
with high fidelity.
