Neural S-matrix bootstrap II: solvable 4d amplitudes with particle production

TL;DR

This paper develops a neural-network-based method to solve nonlinear integral equations for four-dimensional scalar amplitudes, revealing rich physical phenomena including particle production and Regge behavior, and introduces a tuning mechanism for multi-particle effects.

Contribution

It presents a novel neural-network approach to nonperturbative unitarization of 4d scalar amplitudes, incorporating multi-particle data and demonstrating phenomena like Aks screening.

Findings

Obtained a one-parameter family of amplitudes with rich structure.

Demonstrated particle production, Regge behavior, and Landau curves.

Introduced Aks screening to suppress low-spin particle production.

Abstract

We study a model for nonperturbative unitarization of the four-point contact scalar amplitude in four dimensions. It is defined through an infinite sum of planar diagrams, constructed using two-particle unitarity and crossing symmetry. We reformulate the problem in terms of a set of nonlinear integral equations obeyed by the single and double discontinuities of the amplitude. We then solve them using a neural-network ansatz trained by minimizing a physics-informed loss functional. We obtain a one-parameter family of amplitudes, which exhibit rich structure: sizeable particle production, nontrivial emergent Regge behavior, Landau curves, a logarithmic decay at high energy and fixed angle. Finally, we go beyond the two-particle-reducible setup by treating the multi-particle data -- supported above the multi-particle Landau curves due to multi-particle unitarity -- as a dynamical variable. We demonstrate that it can be tuned to suppress low-spin particle production -- a phenomenon we call Aks screening -- at the cost of generating larger and oscillatory double spectral density in the multi-particle region.