Generative networks are opening new avenues in fast event generation for the LHC. We show how generative flow networks can reach percent-level precision for kinematic distributions, how they can be trained jointly with a discriminator, and how this discriminator improves the generation. Our joint training relies on a novel coupling of the two networks which does not require a Nash equilibrium. We then estimate the generation uncertainties through a Bayesian network setup and through conditional data augmentation, while the discriminator ensures that there are no systematic inconsistencies compared to the training data.

翻译：生成网络为LHC的快速事件生成开辟了新的途径。 我们展示了基因流动网络如何达到运动分布的百分率精确度,如何与歧视者共同培训,以及歧视者如何改善这一生成。 我们的联合培训依赖于两个网络的新型组合,而这两个网络并不需要纳什平衡。 然后我们通过建立巴伊西亚网络和通过有条件的数据增强来估计生成的不确定性,而歧视者则确保与培训数据相比不存在系统性的不一致。