论文标题
在没有学习的情况下找到新物理学：异常检测作为在山脉搜索的工具
Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders
论文作者
论文摘要
在本文中，我们提出了一种基于异常检测方法的新策略，以独立于此类新事件的细节而在山脉中搜索新的物理现象。为此，使用标准模型事件对机器学习技术进行了培训，相应的输出对物理敏感。我们探索了HEP中的三种新型AD方法：隔离森林，基于直方图的离群检测和深度支持向量数据描述；以及最习惯的自动编码器。为了评估所提出方法的敏感性，考虑了特定的新物理模型的预测并将其与使用完全监督的深神经网络时所获得的预测。还提出了浅和深度异常检测技术之间的比较。我们的结果表明，半监督异常检测技术的潜力广泛探索了当前和未来的强子围栏数据。
In this paper we propose a new strategy, based on anomaly detection methods, to search for new physics phenomena at colliders independently of the details of such new events. For this purpose, machine learning techniques are trained using Standard Model events, with the corresponding outputs being sensitive to physics beyond it. We explore three novel AD methods in HEP: Isolation Forest, Histogram Based Outlier Detection, and Deep Support Vector Data Description; alongside the most customary Autoencoder. In order to evaluate the sensitivity of the proposed approach, predictions from specific new physics models are considered and compared to those achieved when using fully supervised deep neural networks. A comparison between shallow and deep anomaly detection techniques is also presented. Our results demonstrate the potential of semi-supervised anomaly detection techniques to extensively explore the present and future hadron colliders' data.
