arXiv Machine Learning
The shape of quark flavors
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Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders
arXiv:2607. 24921v1 Announce Type: cross Abstract: Providing a practical and hadron-level definition of multiple jet flavors has been a long-standing challenge in collider physics.
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Efficient AI-Inspired Reduction of Feynman Integrals via Tube Seeding
arXiv:2606. 10698v1 Announce Type: cross Abstract: In this paper, we use machine learning to discover a new seeding strategy for integration-by-parts reduction of Feynman integrals, which is a frequent bottleneck in state-of-the-art calculations in theoretical particle and gravitational-wave physics.
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arXiv Machine Learning
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Variance reduction in lattice QCD observables via normalizing flows
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Learning to Trace Seiberg Dualities
Dualities play an important role in establishing both microscopic and emergent phenomena in a wide range of physical systems. In practice, though, it can often be computationally challenging to establish when two systems are dual, even when all of the "rules of the game" are well-known.