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Explainable AI-assisted Optimization for Feynman Integral Reduction

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arxiv 2502.09544 v1 pith:IFYWSF36 submitted 2025-02-13 hep-ph hep-th

Explainable AI-assisted Optimization for Feynman Integral Reduction

classification hep-ph hep-th
keywords integralsfeynmanreductionsapproachcompareddemonstratesmethodsoptimizing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present a novel approach to optimizing the reduction of Feynman integrals using integration-by-parts identities. By developing a priority function through the FunSearch algorithm, which combines large language models and genetic algorithms, we achieve significant improvements in memory usage and computational efficiency compared to traditional methods. Our approach demonstrates substantial reductions in the required seeding integrals, making previously intractable integrals more manageable. Tested on a variety of Feynman integrals, including one-loop and multi-loop cases with planar and non-planar configurations, our method demonstrates remarkable scalability and adaptability. For reductions of certain Feynman integrals with many dots and numerators, we observed an improvement by a factor of 3058 compared to traditional methods. This work provides a powerful and interpretable framework for optimizing IBP reductions, paving the way for more efficient and practical calculations in high-energy physics.

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    Machine learning discovers a tube-seeding strategy for IBP reduction of Feynman integrals that scales linearly with numerator power, demonstrated on rank-20 2-loop 5-point integrals.

  2. The four-loop non-singlet splitting functions in QCD

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  3. Learning to Unscramble Feynman Loop Integrals with SAILIR

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  4. The spectrum of Feynman-integral geometries at two loops

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    Covariant differentiation on the dual vector space spanned by master integrals reduces a large class of Feynman integrals to masters, with connections reusable across mass configurations.

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    A permutation-equivariant transformer trained on self-supervised oracle trajectories from scrambled expressions achieves near-perfect simplification rates for dilogarithms and 100% success on 5-point gluon scattering ...

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  9. AI-Driven Discovery of Information-Efficient Collider Observables for Interference Measurements

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