A possible probe to neutron-skin thickness by fragment parallel momentum distribution in projectile fragmentation reactions

Neutron-skin thickness is a key parameter for a neutron-rich nucleus;however,it is difficult to determine.In the framework of the Lanzhou Quantum Molecular Dynamics(LQMD)model,a possible probe for the neutron-skin thickness(δ_(np))of neutron-rich ^(48)Ca was studied in the 140A MeV ^(48)Ca+^(9)Be projectile fragmentation reaction based on the parallel momentum distribution(p∥)of the residual fragments.A Fermi-type density distribution was employed to initiate the neutron density distributions in the LQMD simulations.A combined Gaussian function with different width parameters for the left side(Γ_(L))and the right side(Γ_(R))in the distribution was used to describe the p∥of the residual fragments.Taking neutron-rich sulfur isotopes as examples,Γ_(L) shows a sensitive correlation withδ_(np) of ^(48)Ca,and is proposed as a probe for determining the neutron skin thickness of the projectile nucleus.

Determination of neutron-skin thickness using configurational information entropy

Configurational information entropy(CIE)theory was employed to determine the neutron-skin thickness of neutron-rich calcium isotopes.The nuclear density distributions and fragment cross sections in 350 MeV/u ^(40-60)Ca+^(9)Be projectile fragmentation reactions were calculated using a modified statistical abrasion-ablation model.CIE quantities were determined from the nuclear density,isotopic,mass,and charge distributions.The linear correlations between the CIE determined using the isotopic,mass,and charge distributions and the neutron-skin thickness of the projectile nucleus show that CIE provides new methods to extract the neutron-skin thickness of neutron-rich nuclei.

Multiple-models predictions for drip line nuclides in projectile fragmentation of^(40,48)Ca,^(58,64)Ni,and^(78,86)Kr at 140 MeV/u

Modern rare isotope beam(RIB)factories will significantly enhance the production of extremely rare isotopes(ERI)at or near drip lines.As one of the most important methods employed in RIB factories,the production of ERIs in projectile fragmentation reactions should be theoretically improved to provide better guidance for experimental research.The cross-sections of ERIs produced in 140 MeV/u^(78,86)Kr/^(58,64)Ni/^(40,48)Ca+9Be projectile fragmentation reactions were predicted using the newly proposed models[i.e.,Bayesian neural network(BNN),BNN+FRACS,and FRACS,see Chin.Phys.C,46:074104(2022)]and the frequently used EPAX3 model.With a minimum cross-section of 1015 mb,the possibilities of ERIs discovery in a new facility for rare isotope beams(FRIB)are discussed.

Non-frozen process of heavy-ion fusion reactions at deep sub-barrier energies

The hindrance in heavy-ion fusion reactions a deep sub-barrier energies is investigated using the double folding model with a hybrid method between the frozen and adiabatic density approximations.In this method,the density distributions of the projectile and the target depend closely on the distance between them.As the distance decreased,the half-density radii of the colliding nucle gradually increased to the half-density radius of the compound nucleus.The total potential based on this non-frozen approximation generates a slightly shallower pocket and becomes more attractive inside the pocket compared to that obtained from the frozen approximation.A damping factor was used to simulate the decline of the coupled channel effects owing to the density rearrangement of the two colliding nuclei.The calculated fusion cross-sections and astrophysical S factors at the deep sub-barrier energies are both in good agreement with the experimental data for the medium-heavyNi+Ni and medium-lightMg+Si mass systems.In addition,it was concluded that the apparent maximum of the S factors most likely appears in fusion systems with strong coupling effects.

Precise machine learning models for fragment production in projectile fragmentation reactions using Bayesian neural networks

Machine learning models are constructed to predict fragment production cross sections in projectile fragmentation(PF)reactions using Bayesian neural network(BNN)techniques.The massive learning for BNN models is based on 6393 fragments from 53 measured projectile fragmentation reactions.A direct BNN model and physical guiding BNN via FRACS parametrization(BNN+FRACS)model have been constructed to predict the fragment cross section in projectile fragmentation reactions.It is verified that the BNN and BNN+FRACS models can reproduce a wide range of fragment productions in PF reactions with incident energies from 40 MeV/u to 1 GeV/u,reaction systems with projectile nuclei from^40 Ar to^208 Pb,and various target nuclei.The high precision of the BNN and BNN+FRACS models makes them applicable for the low production rate of extremely rare isotopes in future PF reactions with large projectile nucleus asymmetry in the new generation of radioactive nuclear beam factories.

Pauli blocking potential applied to heavy-ion fusion reactions

In this study,the Pauli blocking potential between two colliding nuclei in the density overlapping regionis applied to describe the heavy nuclei fusion process.Inspired by the Pauli blocking effect in theα-decay of heavynuclei,the Pauli blocking potential of single nucleon from the surrounding matter is obtained.In fusion reactionswith strong density overlap,the Pauli blocking potential between the projectile and target can be constructed using asingle folding model.By considering this potential,the double folding model with a new parameter set is employedto analyze the fusion processes of 95 systems.A wider Coulomb barrier and shallower potential pocket are formed inthe inner part of the potential between the two colliding nuclei,compared to that calculated using the Akyüz-Win-ther potential.The fusion hindrance phenomena at deep sub-barrier energies are described well for fusion systems^(16)O+^(208)Pb and^(58)Ni+^(58)Ni.