Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection

TL;DR

This paper compares autoencoder-based unsupervised and CWoLa-based weakly-supervised methods for resonant anomaly detection at the LHC, highlighting their complementary strengths in identifying diverse and rare signals.

Contribution

It provides a detailed comparative analysis of AE and CWoLa methods, demonstrating their complementary effectiveness in resonance anomaly detection.

Findings

CWoLa improves with increasing signal abundance.

AE sensitivity is independent of signal amount.

Combined use enhances detection of diverse signals.

Abstract

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.