Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments
Generic event-level anomaly detection for collider physics has two recurring problems: anomaly scores are hard to interpret, and they correlate strongly with energy scale and object multiplicity. We present Organized Representation via Contrastive learning for Anomaly detection (ORCA), a two-stage framework that first learns an embedding space via supervised contrastive learning across a diverse set of physics processes, then runs a standard autoencoder in that space to generate event-level anomaly scores. On a simulated dataset consistent with conditions at the High-Luminosity Large Hadron Collider, ORCA delivers significant gains in both breadth and depth of sensitivity to new physics s...
| Authors | Haoyi Jia, Sagar Addepalli, Julia Gonski |
|---|---|
| Primary category | cs.LG |
| Published | 13 August 2026 |
| Last updated at source | 26 August 2026 |
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