Determination of electron screening potential of 6 Li(p,{\alpha})3 He reaction using MultiLayer Perceptron based neural network

TL;DR

This paper uses a Multi-Layer Perceptron neural network to analyze experimental data and determine the electron screening potential for the 6 Li(p,α)3 He reaction, providing a novel computational approach in nuclear astrophysics.

Contribution

The study introduces an Artificial Neural Network method to accurately extract the electron screening potential from experimental S-factor data, offering an alternative to traditional estimation techniques.

Findings

Electron screening potential is estimated at 220 eV.

Neural network analysis effectively separates bare and shielded S-factors.

The approach demonstrates potential for analyzing light nuclear reactions in astrophysics.

Abstract

Background: Understanding the nuclear reactions between light charged nuclei at sub-coulomb energy region holds significant importance in several astrophysical processes. Determination of the precise reaction cross-section within the astrophysically important Gamow range is difficult because of electron screening. Various polynomial fits, R-Matrix and Indirect Trojan horse method estimate much higher electron screening energies as compared to the adiabatic limit. Purpose: Obtain the bare astrophysical S-factor of 6 Li(p,{\alpha})3 He using Multi-Layer Perceptron based Artificial Neural Network based analysis and extract the electron screening energies. Methods: Experimental S-factor of 6 Li(p,{\alpha})3 He, available in literature, are reanalyzed using the Multi-LayerPerceptron based Artificial Neural Network based algorithm to obtain the energy dependent astrophysical S-factor. Bare astrophysical S-factor is also calculated using the same Feed-forward Artificial Neural Network from the data range above 60 keV where the electron screening effect is expected to be negligible. Electron screening potential is then obtained by taking the ratio of total shielded S-factor with the bare S-factor. Results and Conclusions: The electron screening potential obtained from the Present work through the Artificial Neural network based algorithm is found to be 220 eV. The extracted electron screening potential through the present analysis indicates that the Artificial Neural Network might be an alternative tools for estimation the electron screening potential involving light nuclei.