Attention to Mean-Fields for Particle Cloud Generation
This work addresses computational bottlenecks in particle physics simulations for future colliders, representing an incremental improvement in domain-specific methods.
The paper tackles the challenge of generating particle clouds for collider data by addressing complex particle correlations and variable cloud sizes, proposing an attention-based model that achieves competitive performance with significantly fewer parameters.
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.