Using generative adversarial networks (GANs), we investigate the possibility
of creating large amounts of analysis-specific simulated LHC events at limited
computing cost. This kind of generative model is analysis specific in the sense
that it directly generates the high-level features used in the last stage of a
given physics analyses, learning the N-dimensional distribution of relevant
features in the context of a specific analysis selection. We apply this idea to
the generation of muon four-momenta in $Z \to \mu\mu$ events at the LHC. We
highlight how use-case specific issues emerge when the distributions of the
considered quantities exhibit particular features. We show how substantial
performance improvements and convergence speed-up can be obtained by including
regression terms in the loss function of the generator. We develop an objective
criterion to assess the geenrator performance in a quantitative way. With
further development, a generalization of this approach could substantially
reduce the needed amount of centrally produced fully simulated events in large
particle physics experiments.

本研究使用生成对抗网络研究在有限计算成本下创建大量分析特定的 LHC 模拟事件的可能性，并针对 Z→μμ 事件生成 muon four-momenta，包括特定分布的问题，展示通过在生成器的损失函数中包含回归术语可以获得明显的性能提升和收敛速度加快，并开发客观标准以定量评估生成器的性能。这种方法的推广将大大减少大型粒子物理实验中所需的完全模拟事件的数量。