  The generation of collider data using machine learning has emerged as a
prominent research topic in particle physics due to the increasing
computational challenges associated with traditional Monte Carlo simulation
methods, particularly for future colliders with higher luminosity. Although
generating particle clouds is analogous to generating point clouds, accurately
modelling the complex correlations between the particles presents a
considerable challenge. Additionally, variable particle cloud sizes further
exacerbate these difficulties, necessitating more sophisticated models. In this
work, we propose a novel model that utilizes an attention-based aggregation
mechanism to address these challenges. The model is trained in an adversarial
training paradigm, ensuring that both the generator and critic exhibit
permutation equivariance/invariance with respect to their input. A novel
feature matching loss in the critic is introduced to stabilize the training.
The proposed model performs competitively to the state-of-art whilst having
significantly fewer parameters.
