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Similarity Pairing with Energy Mover's Distance for Self-Supervised Pre-Training at the LHC

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

Many self-supervised methods for training foundation models at the Large Hadron Collider (LHC) rely on data augmentations to encourage the model to embed events into a representation space invariant to certain physical or detector symmetries. A common challenge arises from the large freedom in choosing a proper set of augmentations on which downstream performance depends. The implementation of augmentations involves either modifying existing events, potentially breaking the event fidelity, or simulating more event variants, which is computationally intensive. In this work, we present a data-dr

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