Spinning Conformal Correlators from Neural Networks
Manas Dogra, James Halverson, Joydeep Naskar
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 Halverson:2024axc. For a particular ensemble of i.i.d. neurons we recover the 4d Maxwell CFT in the infinite-width limit.
Create a lesson
Related papers
When duality changes the poles: SL(2,Z) transformations of linear response EFTs
Andrea Amoretti, Daniel K. Brattan, Jonas Rongen
The Geometry of the CKM matrix, the Standard Model and RG fixed points
Brian P. Dolan, Charles Nash
Auxiliary Field Deformations of the Lambda Model
Christian Ferko, Cian Luke Martin, Pranat Sharma
Carrollian axion electrodynamics
Hemant Rathi, Ashish Shukla
The geometry of multiloop Feynman Integrals from the Scattering Facet
José Ríos-Sánchez, Germán Rodrigo
Deep learning emergent spacetime from fermionic spectral functions in holography
Koji Hashimoto, Hyun-Sik Jeong, Keun-Young Kim et al.