AI 中文总结
该研究针对LHC模拟，利用生成式网络技术量化替代蒙特卡洛的生成式放大效应，发现其在胶子伴随Z产生的稀疏运动学尾部表现显著，且效果优于现有生成式网络的密度估计。
AI 中文摘要
大型强子对撞机（LHC）模拟用的振幅替代模型以生成式放大为基础，该现象指在昂贵且规模小的训练数据集上训练出的替代模型，能比训练数据更精准地描述平滑振幅。本文将生成式网络相关技术应用于胶子伴随Z玻色子产生过程，对该放大效应进行量化，发现在最关键的运动学稀疏尾部存在显著放大，结果表明替代蒙特卡洛的生成式放大效果远超当前生成式网络的密度估计。
英文摘要
Amplitude surrogates for LHC simulations build on generative amplification, the fact that a surrogate trained on an expensive and small training dataset describes the smooth amplitude more precisely than the training data does. Applying techniques developed for generative networks, we quantify this amplification for gluon-associated $Z$ production. Significant amplification appears in sparsely populated kinematic tails, where it matters most. Our results show how generative amplification from surrogate Monte Carlo far outperforms the density estimation in current generative networks.
Comments23 pages, 17 figures
