Extracting neutron skin from elastic proton-nucleus scattering with deep neural network
Based on the relativistic impulse approximation of proton-nucleus elastic scattering theory, the neutron density distribution and neutron skin thickness of 48Ca are estimated via the deep learning method. The neural-network-generated neutron densities are mainly compressed to be higher inside the nu...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Physics Letters B |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0370269325000619 |
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| Summary: | Based on the relativistic impulse approximation of proton-nucleus elastic scattering theory, the neutron density distribution and neutron skin thickness of 48Ca 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 48Ca is captured to be 0.199(17) fm. The relatively thicker neutron skin is deemed reasonable from the perspective of density functional analysis. |
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| ISSN: | 0370-2693 |