标题： Wasserstein 归一化自编码器用于异常检测 显示英文标题

标题： Wasserstein normalized autoencoder for anomaly detection

摘要： 一种新的异常检测算法被提出。 Wasserstein归一化自编码器（WNAE）是一个归一化的概率模型，它最小化学习到的概率分布与训练数据分布之间的Wasserstein距离——该概率分布是一个玻尔兹曼分布，其中能量是自编码器的重构误差。 该算法已开发并应用于CMS实验中半可见喷注的识别——即可见标准模型粒子和不可见暗物质状态的锥形喷注。 在模拟的标准模型过程产生的喷注上进行训练，WNAE被证明能够以完全无监督的方式学习输入数据的概率分布，从而有效地将新物理喷注识别为异常。 该模型在各种信号上表现出稳定且收敛的训练，并实现了强大的分类性能，优于标准的归一化自编码器，同时对信号保持无知。 WNAE直接解决了异常检测任务中的离群值重构问题，这是自编码器在异常检测任务中的常见失败模式。 显示英文摘要

摘要： A novel anomaly detection algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between the learned probability distribution -- a Boltzmann distribution where the energy is the reconstruction error of the autoencoder -- and the distribution of the training data. This algorithm has been developed and applied to the identification of semivisible jets -- conical sprays of visible standard model particles and invisible dark matter states -- with the CMS experiment at the CERN LHC. Trained on jets of particles from simulated standard model processes, the WNAE is shown to learn the probability distribution of the input data in a fully unsupervised fashion, such that it effectively identifies new physics jets as anomalies. The model consistently demonstrates stable, convergent training and achieves strong classification performance across a wide range of signals, improving upon standard normalized autoencoders, while remaining agnostic to the signal. The WNAE directly tackles the problem of outlier reconstruction, a common failure mode of autoencoders in anomaly detection tasks.