Speaker
Description
Weakly supervised anomaly detection has been shown to have great potential for improving traditional resonance searches. We demonstrate that weak supervision offers a unique opportunity to turn a resonance search into a simple cut-and-count experiment, where the potential problem of background sculpting in a traditional bump hunt is absent. Moreover, the cut-and-count setting allows working with large background rejection rates, where weakly supervised methods typically show their greatest significance improvement. Our method also provides a simple way to benchmark weakly supervised anomaly detection approaches in an end-to-end application. We quantify the performance of such a cut-and-count search using the CWoLa and Cathode approaches on the LHC Olympics R&D dataset.
Track | Anomaly detection |
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