论文标题
IRC安全图自动编码器，用于无监督的异常检测
IRC-safe Graph Autoencoder for unsupervised anomaly detection
论文作者
论文摘要
通过使用机器学习技术的异常检测已成为一种新型强大的工具，可以在标准模型之外寻找新物理学。从历史上看，与Jet可观察物的发展相似，理论一致性并不总是在算法和神经网络体系结构的快速发展中扮演核心作用。在这项工作中，我们通过使用能量加权消息传递来构建基于图神经网络的红外和共线安全自动编码器。我们证明，尽管这种方法具有理论上有利的特性，但它也对非QCD结构具有强大的敏感性。
Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of jet observables, theoretical consistency has not always assumed a central role in the fast development of algorithms and neural network architectures. In this work, we construct an infrared and collinear safe autoencoder based on graph neural networks by employing energy-weighted message passing. We demonstrate that whilst this approach has theoretically favourable properties, it also exhibits formidable sensitivity to non-QCD structures.
