Work

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

Year: 2025

Type: article

Abstract: Based on the relativistic impulse approximation of proton-nucleus elastic scattering theory, the neutron density distribution and neutron skin thickness of 48 Ca are estimated via the deep learning method. The neural-network-generated neutron densities are mainly compressed to be higher 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 0.199(17) fm. The relatively thicker neutron skin is deemed reasonable from the perspective of density functional analysis.

Source: Physics Letters B

DOI: 10.1016/j.physletb.2025.139301

Authors: G.H. Yang, Y. Kuang, Z.X. Yang, Z.P. Li

Institutions: Southwest University, RIKEN Nishina Center

Language: English

Topic: Nuclear physics research studies

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities

Awards: G53379541, G6104968626