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Opportunities for production and property research of neutron-rich nuclei around N=126 at HIAF
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作者 Shao-Bo Ma Li-Na Sheng +9 位作者 Xue-Heng Zhang Shi-Tao Wang Kai-Long Wang Chun-Wang Ma Hool-Jin Ong Zhi-Yu Sun Shu-Wen Tang Yu-Hong Yu Xin-Tong Du xiao-bao wei 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2024年第6期101-113,共13页
The study of nuclide production and its properties in the N=126 neutron-rich region is prevalent in nuclear physics and astrophysics research.The upcoming High-energy FRagment Separator(HFRS)at the High-Intensity heav... The study of nuclide production and its properties in the N=126 neutron-rich region is prevalent in nuclear physics and astrophysics research.The upcoming High-energy FRagment Separator(HFRS)at the High-Intensity heavy-ion Accelerator Facility(HIAF),an in-flight separator at relativistic energies,is characterized by high beam intensity,large ion-optical acceptance,high magnetic rigidity,and high momentum resolution power.This provides an opportunity to study the production and properties of neutron-rich nuclei around N=126.In this paper,an experimental scheme is proposed to produce neutron-rich nuclei around N=126 and simultaneously measure their mass and lifetime based on the HFRS separator;the feasibility of this scheme is evaluated through simulations.The results show that under the high-resolution optical mode,many new neutron-rich nuclei approaching the r-process abundance peak around A=195 can be produced for the first time,and many nuclei with unknown masses and lifetimes can be produced with high statistics.Using the time-of-flight corrected by the measured dispersive position and energy loss information,the cocktails produced from 208 Pb fragmentation can be unambiguously identified.Moreover,the masses of some neutron-rich nuclei near N=126 can be measured with high precision using the time-of-flight magnetic rigidity technique.This indicates that the HIAF-HFRS facility has the potential for the production and property research of neutron-rich nuclei around N=126,which is of great significance for expanding the chart of nuclides,developing nuclear theories,and understanding the origin of heavy elements in the universe. 展开更多
关键词 HFRS FRAGMENTATION Neutron-rich nuclei around N=126 Mass measurement LIFETIME
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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 被引量:5
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作者 xiao-bao wei Hui-Ling wei +4 位作者 Yu-Ting Wang Jie Pu Kai-Xuan Cheng Ya-Fei Guo Chun-Wang Ma 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2022年第12期49-58,共10页
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 ER... 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. 展开更多
关键词 Bayesian neural network(BNN) FRACS Drip line Extremely rare isotope Projectile fragmentation
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Precise machine learning models for fragment production in projectile fragmentation reactions using Bayesian neural networks 被引量:7
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作者 Chun-Wang Ma xiao-bao wei +6 位作者 Xi-Xi Chen Dan Peng Yu-Ting Wang Jie Pu Kai-Xuan Cheng Ya-Fei Guo Hui-Ling wei 《Chinese Physics C》 SCIE CAS CSCD 2022年第7期118-128,共11页
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 ... 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. 展开更多
关键词 projectile fragmentation rare isotope machine learning Bayesian neural network drip line cross section radioactive nuclear beam
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