Fixed-energy inverse scattering with radial basis function neural networks and its application to neutron-alpha interactions

TL;DR

This paper introduces a neural network-based approach using radial basis functions to solve the fixed-energy inverse scattering problem, successfully estimating neutron-alpha interaction potentials from phase shift data.

Contribution

It presents a novel data-driven method employing RBF neural networks for inverse scattering, capable of modeling various scattering events and applied to neutron-alpha interactions.

Findings

Estimated potential is physically sensible

Recalculated phase shifts match measured data within a few percent

Method effectively models scattering events using phase shift inputs

Abstract

This paper proposes a data-driven method to solve the fixed-energy inverse scattering problem for radially symmetric potentials using radial basis function (RBF) neural networks in an open-loop control system. The method estimates the scattering potentials in the Fourier domain by training an appropriate number of RBF networks, while the control step is carried out in the coordinate space by using the measured phase shifts as control parameters. The system is trained by both finite and singular input potentials and is capable of modeling a great variety of scattering events. The method is applied to neutron-alpha scattering at 10 MeV incident neutron energy, where the underlying central part of the potential is estimated by using the measured l = 0, 1, 2 phase shifts as inputs. The obtained potential is physically sensible, and the recalculated phase shifts are within a few percent relative error.