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.
翻译:通过使用机器学习技术进行异常探测已成为在标准模型之外寻找新物理学的新的有力工具。历史上,理论一致性与喷射观测系统的发展类似,在算法和神经网络结构的快速发展方面,理论一致性并不总是发挥中心作用。在这项工作中,我们利用能量加权信息传递的方式,在图形神经网络的基础上,建造红外和山线安全自动编码器。我们证明,虽然这种方法在理论上具有有利的特性,但对非QCD结构也表现出极大的敏感性。