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.

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在对撞机上搜索新的物理现象。为此，使用标准模型事件来训练机器学习技术，并且相应的输出对超出其范围的物理敏感。我们在HEP​​中探索了三种新颖的AD方法：隔离林，基于直方图的离群值检测和深度支持向量数据描述；以及最常用的自动编码器。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度神经网络时所获得的预测进行了比较。还介绍了浅层和深层异常检测技术之间的比较。