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