Attention to Mean-Fields for Particle Cloud Generation

Understand

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.