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2021

Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection

Collins, Jack H., Martín-Ramiro, Pablo, Nachman, Benjamin et al.

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Anomaly detection techniques are growing in importance at the Large Hadron Collider (LHC), motivated by the increasing need to search for new physics in a model-agnostic way.

In this work, we provide a detailed comparative study between a well-studied unsupervised method called the autoencoder (AE) and a weakly-supervised approach based on the Classification Without Labels (CWoLa) technique.
We examine the ability of the two methods to identify a new physics signal at different cross sections in a fully hadronic resonance search.
By construction, the AE classification performance is independent of the amount of injected signal.

Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection · Around

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