用于异常喷流标记的变分自编码器
Variational Autoencoders for Anomalous Jet Tagging
摘要：
本文详细研究了大型强子对撞机异常喷注标记的变分自编码器（VAE）。通过获取低空喷流成分的信息，并以无监督的方式使用背景QCD喷流进行训练，VAE能够对重建喷流的重要信息进行编码，同时学习潜在空间中富有表现力的后验分布。当使用VAE作为异常检测器时，我们提出了不同的方法来检测异常：直接在输入空间中进行比较，或者在潜在空间中进行比较。为了方便一般的搜索方法，如碰撞狩猎，质量去相关的VAE距离相关正则化的基础上进行了研究。我们发现，天真的质量去相关的VAE未能保持适当的检测性能，分配更高的概率，一些异常的样本。为了建立一个高性能的质量去相关异常射流标签，我们提出了离群暴露VAE（OE-VAE），其中一些离群样本被引入到训练过程中，以指导学习的信息。OE-VAE同时实现两个目标：增加异常值检测的灵敏度和将射流质量与异常评分去相关。我们在这两个方面都取得了很好的成绩。此工作的代码实现可以在https://github.com/taolicheng/VAE-Jet上找到
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
