Nonautonomous Volterra Series Expansion of the Variable Phase Approximation and its Application to the Nucleon-Nucleon Inverse Scattering Problem

TL;DR

This paper extends the nonlinear Volterra series to nonautonomous differential equations for inverse scattering in nuclear physics, combining neural networks to improve phase shift modeling and accurately reconstruct NN potentials.

Contribution

It introduces a novel nonautonomous Volterra series expansion method combined with neural networks for inverse scattering, enabling accurate potential reconstruction from phase shifts.

Findings

Achieved below 1% average relative error in phase shift modeling.

Successfully reconstructed 1S0 NN potentials below 200 MeV.

Provided physically meaningful potentials consistent with experimental data.

Abstract

In this paper, the nonlinear Volterra series expansion is extended and used to describe certain types of nonautonomous differential equations related to the inverse scattering problem in nuclear physics. The nonautonomous Volterra series expansion lets us determine a dynamic, polynomial approximation of the variable phase approximation (VPA), which is used to determine the phase shifts from nuclear potentials through first-order nonlinear differential equations. By using the first-order Volterra expansion, a robust approximation is formulated to the inverse scattering problem for weak potentials and/or high energies. The method is then extended with the help of radial basis function neural networks by applying a nonlinear transformation on the measured phase shifts to be able to model the scattering system with a linear approximation given by the first-order Volterra expansion. The method is applied to describe the 1S0 NN potentials in neutron+proton scattering below 200 MeV laboratory kinetic energies, giving physically sensible potentials and below 1% averaged relative error between the recalculated and the measured phase shifts.