Optimal Transport Event Representation for Anomaly Detection

TL;DR

This paper proposes using optimal transport as a physics-inspired event representation to improve weakly supervised anomaly detection in high-energy physics, significantly enhancing detection significance over standard methods.

Contribution

It introduces a novel OT-based event representation that outperforms traditional high-level observables in weakly supervised anomaly detection tasks.

Findings

OT-augmented features nearly double significance improvement

End-to-end deep learning struggles with low signals

Structured representations enhance anomaly detection robustness

Abstract

We introduce optimal transport (OT) as a physics-based intermediate event representation for weakly supervised anomaly detection. With only $0.5\%$ injection of resonant signals in the LHC Olympics benchmark datasets, the OT-augmented feature set achieves nearly twice the significance improvement of standard high-level observables provided in the benchmark, while end-to-end deep learning on low-level four-momenta struggles in the low-signal regime. The gains persist across signal types and classifiers, underscoring the value of structured representations in machine learning for anomaly detection.