Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders

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. 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 present and future hadron colliders' data.