Speaker
Description
Weakly supervised anomaly detection has been shown to be an effective tool for model agnostic searches for new physics, especially in the context of resonance searches. However, to integrate weak supervision into standard resonance search analysis workflows, which rely on fits in the sidebands for the background estimation, anomaly scores need to be well behaved in the sidebands. In order to achieve this for any weakly supervised anomaly detection method, we propose a domain adaptation-based decorrelation of the anomaly detection score from the resonant mass. We demonstrate the effectiveness of this method to not only reduce background sculpting but also recover anomaly detection performance in the presence of correlated features using the LHC Olympics R\&D data set for both CATHODE and CWoLa Hunting.