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.

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.