  Data generation based on Machine Learning has become a major research topic
in particle physics. This is due to the current Monte Carlo simulation approach
being computationally challenging for future colliders, which will have a
significantly higher luminosity. The generation of collider data is similar to
point cloud generation, but arguably more difficult as there are complex
correlations between the points which need to be modelled correctly. A
refinement model consisting of normalising flows and transformer encoders is
presented. The normalising flow output is corrected by a transformer encoder,
which is adversarially trained against another transformer encoder
discriminator/critic. The model reaches state-of-the-art performance while
yielding a stable training.
