  In the field of modern high-energy physics research, there is a growing
emphasis on utilizing deep learning techniques to optimize event simulation,
thereby expanding the statistical sample size for more accurate physical
analysis. Traditional simulation methods often encounter challenges when
dealing with complex physical processes and high-dimensional data
distributions, resulting in slow performance. To overcome these limitations, we
propose a solution based on deep learning with the sliced Wasserstein distance
as the loss function. Our method shows its ability on high precision and
large-scale simulations, and demonstrates its effectiveness in handling complex
physical processes. By employing an advanced transformer learning architecture,
we initiate the learning process from a Monte Carlo sample, and generate
high-dimensional data while preserving all original distribution features. The
generated data samples have passed the consistence test, that is developed to
calculate the confidence of the high-dimentional distributions of the generated
data samples through permutation tests. This fast simulation strategy, enabled
by deep learning, holds significant potential not only for increasing sample
sizes and reducing statistical uncertainties but also for applications in
numerical integration, which is crucial in partial wave analysis,
high-precision sample checks, and other related fields. It opens up new
possibilities for improving event simulation in high-energy physics research.
