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Neural networks construct spinning conformal fields, recovering Maxwell CFT

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Neural networks construct spinning conformal fields, recovering Maxwell CFT

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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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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Manas Dogra, James Halverson, Joydeep Naskar ·

    Spinning Conformal Correlators from Neural Networks

    arXiv:2608.15001v1 Announce Type: cross Abstract: We construct spinning conformal fields from neural networks and the embedding formalism, computing their two-, three- and four-point functions in examples, building on scalar conformal field techniques introduced in \cite{Halverso…

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