Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
This work addresses the need for model-agnostic new physics searches in particle physics, but it is incremental as it compares existing methods rather than introducing new ones.
The study compared unsupervised autoencoder (AE) and weakly-supervised Classification Without Labels (CWoLa) methods for resonant anomaly detection at the Large Hadron Collider, finding that AE performance is independent of signal amount while CWoLa improves with more signal, and they have complementary sensitivity when integrated with background estimates.
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. In contrast, the CWoLa performance improves with increasing signal abundance. When integrating these approaches with a complete background estimate, we find that the two methods have complementary sensitivity. In particular, CWoLa is effective at finding diverse and moderately rare signals while the AE can provide sensitivity to very rare signals, but only with certain topologies. We therefore demonstrate that both techniques are complementary and can be used together for anomaly detection at the LHC.