  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.
