|
Résumé |
Can a simple neural network deduce the full functional form of a conformal field
theory (CFT) correlation function from almost nothing? In this talk, I will
introduce the Anchored Neural Conformal Bootstrap, a remarkably simple yet highly
effective computational method for reconstructing CFT correlators. I will
demonstrate that by training a neural network using only crossing symmetry,
correlator asymptotics, and a single “anchor point”, one can successfully recover
correlators across a wide range of theories. I will argue that this success is
driven by the spectral bias of neural networks, which inherently focuses on
maximally smooth functions. This suggests a potential new aspect of CFTs: their
correlators are among the smoothest crossing-symmetric functions. |
