Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments
The paper introduces ORCA, a two-stage framework combining supervised contrastive learning with autoencoders to achieve both improved sensitivity and interpretability in collider anomaly detection by mapping events into an embedding space where anomalous samples can be quantitatively attributed to known physics processes.