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Extracting Neutron Skin from Elastic Proton-nucleus Scattering with Deep Neural Network by Yang, G. H.; Kuang, Y.; Yang, Z. X.; Li, Z. P. is a scholarly article available to read on EtoBox.

What is Extracting Neutron Skin from Elastic Proton-nucleus Scattering with Deep Neural Network about?

Based on the relativistic impulse approximation of proton-nucleus elastic scattering theory, the nucleon density distribution and neutron skin thickness of $^{48}$Ca are estimated via the deep learning method. The neural-network-generated densities are mainly compressed to be lower inside the nucleus compared with the results from the relativistic PC-PK1 density functional, resulting in a significant improvement on the large-angle scattering observables, both for the differential cross section and analyzing power. The neutron skin thickness of $^{48}$Ca is captured to be 0.211(11) fm. The relatively thicker neutron skin is deemed reasonable from the perspective of density functional analysis.

Author

Yang, G. H.; Kuang, Y.; Yang, Z. X.; Li, Z. P.

Published

2023

Language

EN