Estimating Inverse Scattering Potentials for n-p System Using Variational Monte Carlo & Neural Networks
Anil Khachi, Gabor Balassa

TL;DR
This paper develops a method to estimate inverse scattering potentials for neutron-proton systems using Variational Monte Carlo and neural networks, achieving accurate phase shift modeling with reduced computational effort.
Contribution
It introduces a novel combination of VMC and neural networks to optimize Morse potential parameters for inverse scattering, simplifying the optimization process.
Findings
VMC and NN produce nearly identical potential parameters.
The NN approach reduces optimization parameters and computational cost.
The method accurately fits experimental phase shift data.
Abstract
The Riccati-type nonlinear differential equation, also known as the Variable Phase Approach or Phase Function Method, is used to construct local inverse potentials for the \( ^3S_1 \) and \( ^1S_0 \) states of the deuteron. The Morse potential has been optimized by adjusting parameters using the Variational Monte Carlo (VMC) and Multilayer Perceptron (MLP) type Neural Networks (NN). The inverse potentials obtained from VMC and NN show almost identical parameters. In VMC, all three parameters of the Morse potential are varied to obtain the phase shifts, while in NN, the 3D-parameter optimization problem is converted to a 1D-parameter optimization problem, thus reducing optimization parameters, time, and computational cost. Recently, the GRANADA group published a comprehensive partial wave analysis of scattering data, which includes 6713 \( np \) phase shift data points from 1950 to…
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Taxonomy
TopicsImage and Signal Denoising Methods · Seismic Imaging and Inversion Techniques
