论文标题
异常喷气标记的变异自动编码器
Variational Autoencoders for Anomalous Jet Tagging
论文作者
论文摘要
我们介绍了一项关于在大型强子对撞机上进行异常的射流标记的差异自动编码器（VAE）的详细研究。通过以无监督的方式获取低级JET成分的信息，并使用背景QCD喷气机进行训练，VAE能够编码重要信息以重建喷气机，同时学习潜在空间中的表现性后验分布。当使用VAE作为异常检测器时，我们提出了不同的检测异常方法：直接在输入空间中进行比较，或者在潜在空间中工作。还研究了基于距离相关正则化的一般搜索方法，例如碰撞狩猎，质量与质量相关的VAE。我们发现，通过为某些异常样本分配更高的概率，幼稚的质量与质量相关的VAE无法保持适当的检测性能。为了构建表演者质量相关的异常喷射标签器，我们提出了离群的暴露vae（OE-VAE），为此在培训过程中引入了一些离群样本以指导学习的信息。使用OE-VAE同时实现了两个目标：从异常得分中提高异常检测的敏感性和射流质量的敏感性。我们成功地从这两个方面取得了出色的成果。可以在https://github.com/taolicheng/vae-jet上找到这项工作的代码实施。
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
