We present a detailed study on Variational Autoencoders (VAEs) for anomalous jet tagging at the Large Hadron Collider. By taking in low-level jet constituents' information, and training with background QCD jets in an unsupervised manner, the VAE is able to encode important information for reconstructing jets, while learning an expressive posterior distribution in the latent space. When using the VAE as an anomaly detector, we present different approaches to detect anomalies: directly comparing in the input space or, instead, working in the latent space. In order to facilitate general search approaches such as bump-hunt, mass-decorrelated VAEs based on distance correlation regularization are also studied. We find that the naive mass-decorrelated VAEs fail at maintaining proper detection performance, by assigning higher probabilities to some anomalous samples. To build a performant mass-decorrelated anomalous jet tagger, we propose the Outlier Exposed VAE (OE-VAE), for which some outlier samples are introduced in the training process to guide the learned information. OE-VAEs are employed to achieve two goals at the same time: increasing sensitivity of outlier detection and decorrelating jet mass from the anomaly score. We succeed in reaching excellent results from both aspects. Code implementation of this work can be found at https://github.com/taolicheng/VAE-Jet

翻译：我们详细研究了大型高原对流机反常喷射喷射标记的变形自动镜(VAEs)问题。为了便于采用一般搜索方法,例如低空喷射成分信息,以及以不受监督的方式进行与背景QCD喷射机有关的培训,VAE能够对重建喷射机的重要信息进行编码,同时在潜藏空间学习一种显性外表分布。当使用VAE作为异常探测器时,我们提出了不同的方法来探测异常现象:直接比较输入空间,或相反,在潜伏空间工作。为了便于采用基于远程相关规范的低空喷射成品、与质量成品相关的VAE斯相关的一般搜索方法。我们发现,天性质量成品相关的VAE无法保持适当的检测性能,对一些反常样品进行更高的概率。为了建立一个与超常质量的定性的模拟展览(O-VAE),我们建议从这种超常值样本中获取了超常值的超常性E级测试结果。