Semi-supervision in Machine Learning can be used in searches for new physics where the signal plus background regions are not labelled. This strongly reduces model dependency in the search for signals Beyond the Standard Model. This approach displays the drawback in that over-fitting can give rise to fake signals. Tossing toy Monte Carlo (MC) events can be used to estimate the corresponding trials factor through a frequentist inference. However, MC events that are based on full detector simulations are resource intensive. Generative Adversarial Networks (GANs) can be used to mimic MC generators. GANs are powerful generative models, but often suffer from training instability. We henceforth show a review of GANs. We advocate the use of Wasserstein GAN (WGAN) with weight clipping and WGAN with gradient penalty (WGAN-GP) where the norm of gradient of the critic is penalized with respect to its input. Following the emergence of multi-lepton anomalies at the LHC, we apply GANs for the generation of di-leptons final states in association with b-quarks at the LHC. A good agreement between the MC events and the WGAN-GP events is found for the observables selected in the study.

翻译：机器学习中的半监视器可用于寻找没有标记信号加背景区域的新物理学。 这极大地减少了在标准模型之外寻找信号时的模型依赖性。 这种方法显示了过度安装会产生假信号的缺点。 可以通过经常推论来使用玩具蒙特卡洛(MC)事件来估计相应的试验系数。 但是, 以完全检测器模拟为基础的MC事件是资源密集型的。 基因反转网络(GANs)可以用来模仿 MC 发电机。 GANs 是强大的基因化模型,但往往受到训练不稳定的影响。 我们从此展示了对GANs的审查。 我们主张使用瓦瑟斯坦GAN(WGAN), 其重量剪切, 并使用具有梯度的WGAN(WGAN-GP) 来估计相应的试验系数。 在LHC出现多 Lepton 异常后, 我们应用GANs 来生成与b- quarks相关的二 Lepton最后状态, 但却常常受到训练不稳定的影响。 我们主张使用Wasserstein GAN(WG) 和所选的LHCHA 之间的良好协议。