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, 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 and the WGAN-GP generated 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搜索信号时的模型依赖性。 这个方法显示了超配可能导致虚假信号的缺点。 使用玩具蒙蒙特卡洛(MC)事件可以通过常客推论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,但往往受到培训不稳定的影响。 我们从此展示了对GANs的审查。 我们主张使用瓦瑟斯坦GAN(WGAN), 其重量剪裁剪贴, 而WGAN(WGAN-GP) 则在评论器的梯度标准上对其输入进行处罚。 在多 Lepton 异常出现后, 我们应用GANs 来生成与$b$-quarks连带的dilepton最后状态, 但它往往受到训练不稳定的影响。 我们主张使用Wasserrstein GAN(WGAN) 和在LHCS-GM 中选定的活动之间达成良好协议。