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G-SpaNet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention
Alexander Shmakov · Shih-chieh Hsu · Pierre Baldi

We introduce a novel method for constructing symmetry-preserving attention networks which reflect the natural invariances of the jet-parton assignment problem to efficiently find assignments without evaluating all permutations. This general approach is applicable to arbitrarily complex configurations and significantly outperforms current methods, improving reconstruction efficiency between 19% - 35% on benchmark problems while decreasing inference time by two to five orders of magnitude, making many important and previously intractable cases tractable.

Author Information

Alexander Shmakov (University of California Irvine)
Shih-chieh Hsu (University of Washington)
Pierre Baldi (UC Irvine)

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