collider-physics

ORCA is a two-stage framework using contrastive learning and autoencoders for interpretable anomaly detection in collider experiments, enhancing sensitivity and interpretability for new physics searches.

Presents NEXUS, a lightweight foundation model with ~3M parameters pre-trained on LHC collision data, demonstrating improved downstream performance and cross-domain transfer to gravitational waves, flood forecasting, and neural activity.