Extracting neutron skin from elastic proton-nucleus scattering with deep neural network

2023/11/20 by Yang, G. H., Ye Kuang, Kuang, Y. +4
Engineering · Physics and Astronomy · #FOS: Physical sciences #Nuclear Physics and Applications #Nuclear Theory (nucl-th) #Nuclear physics research studies #Superconducting Materials and Applications

paper · pdf · doi:10.48550/arxiv.2311.11676

openalex publication_date 2023/11/20 · openalex created_date 2023/11/23 · openalex updated_date 2026/07/28

Abstract

Based on the relativistic impulse approximation of proton-nucleus elastic scattering theory, the nucleon density distribution and neutron skin thickness of ⁴⁸Ca 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 ⁴⁸Ca is captured to be 0.211(11) fm. The relatively thicker neutron skin is deemed reasonable from the perspective of density functional analysis.

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