Pay Attention to Mean Fields for Point Cloud Generation

Collider data generation via machine learning is gaining traction in particle physics due to the computational cost of traditional Monte Carlo simulations, especially for future high-luminosity colliders. This study presents a model using linearly scaling attention-based aggregation. The model is trained in an adversarial setup, ensuring input permutation equivariance respective invariance for the generator and critic, respectively. A feature matching loss is introduced to stabilise known unstable adversarial training. Results are presented for two different datasets. On the \textsc{JetNet150} dataset, the model is competitive but more parameter-efficient than the current state-of-the-art GAN-based model. The model has been extended to handle the CaloChallenge Dataset 2, where each point cloud contains up to $30\times$ more points than for the previous dataset. The model and its corresponding code will be made available upon publication.