Building on the success of PC-JeDi we introduce PC-Droid, a substantially improved diffusion model for the generation of jet particle clouds. By leveraging a new diffusion formulation, studying more recent integration solvers, and training on all jet types simultaneously, we are able to achieve state-of-the-art performance for all types of jets across all evaluation metrics. We study the trade-off between generation speed and quality by comparing two attention based architectures, as well as the potential of consistency distillation to reduce the number of diffusion steps. Both the faster architecture and consistency models demonstrate performance surpassing many competing models, with generation time up to two orders of magnitude faster than PC-JeDi and three orders of magnitude faster than Delphes.

翻译：基于PC-JeDi的成功，我们提出PC-Droid——一个显著改进的扩散模型，用于喷注粒子云的生成。通过采用新的扩散公式、研究更新的积分求解器，并同时对所有喷注类型进行训练，我们能够在所有评估指标上为各类喷注实现最先进的性能。我们通过比较两种基于注意力机制的架构，以及一致性蒸馏在减少扩散步数方面的潜力，研究了生成速度与质量之间的权衡。更快的架构和一致性模型均展现出远超众多竞争模型的性能，生成速度比PC-JeDi快两个数量级，比Delphes快三个数量级。