Speakers
Description
Foundation models have recently emerged as a promising approach for learning transferable representations from low-level particle physics data. In this work, we investigate the application of the OmniJet-α foundation model, to jet anomaly detection, focusing on how pretraining strategies and downstream optimization affect performance on the LHC Olympics (LHCO) benchmark. We systematically compare supervised and self-supervised pretraining objectives and explore transfer-learning strategies, including batch composition and model-selection criteria for downstream anomaly detection. Together, these studies identify effective approaches for transferring foundation models to jet anomaly detection. Beyond LHCO, we investigate the generalization of the optimized models using benchmark signal samples from the CMS anomaly detection analysis, establishing a benchmark for future studies of foundation models in fully agnostic collider searches. These findings highlight the potential of foundation models to enhance fully agnostic anomaly detection in collider physics.