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.

翻译：利用机器学习生成对撞机数据已成为粒子物理学中的一个重要研究课题，原因是传统蒙特卡罗模拟方法（尤其针对未来高亮度对撞机）所需计算资源日益增长。尽管粒子云生成与点云生成具有相似性，但准确建模粒子间复杂关联仍构成重大挑战。此外，粒子云尺寸的可变性进一步加剧了这些困难，需要更精密的模型。本研究提出一种基于注意力聚合机制的新型模型以应对这些挑战。该模型采用对抗训练范式，确保生成器与判别器均对其输入保持置换等变性/不变性。我们引入了一种新颖的特征匹配损失函数以稳定判别器的训练过程。所提模型在显著减少参数量的同时，实现了与当前最优方法相匹敌的性能。