Neural networks construct spinning conformal fields, recovering Maxwell CFT

Researchers have developed a method to construct spinning conformal fields using neural networks and the embedding formalism. This approach allows for the computation of two-, three-, and four-point functions, building upon existing scalar conformal field techniques. In a specific scenario with independent and identically distributed neurons, the method successfully reproduces the 4D Maxwell Conformal Field Theory in the infinite-width limit. AI

IMPACT Introduces a novel application of neural networks in theoretical physics, potentially advancing research in conformal field theory and high-energy physics.

RANK_REASON The item is an academic paper on arXiv detailing a new method for constructing spinning conformal fields using neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Halverson:2024axc
• high energy physics
• Hugging Face
• Maxwell CFT
• neural networks

• paper
• other

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