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Accurate Prediction of Cross-section of Extremely Rare Isotope
Accurate Prediction of Cross-section of Extremely Rare Isotope

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


Xiao-Bao Wei;Hui-Ling Wei;Yu-Ting Wang;Jie Pu;Kai-Xuan Cheng;Ya-Fei Guo;Chun-Wang Ma


Nuclear Science and Techniques  Vol.33, Issue 12, Article number:155 (2022)




Plain Language Summary                    


The Novelty

To pursue the exploration of drip line nuclides, this study performed multiple-models predictions to predict the cross-sections of extremely rare isotope (ERI) produced in typical projectile fragmentation (PF) reactions in the Facility for Rare Isotope Beam (FRIB), namely 78,86Kr + 9Be, 58,64Ni + 9Be, and 40,48Ca + 9Be reactions at 140 MeV/u. The multiple-models predictions comprised the EPAX3, FRACS, Bayesian neural network technology (BNN), and BNN + FRACS models. Results showed that both neutron and proton drip lines can be reached for elements of atomic number ≤ 11 with the lowest cross-section of 10−15 mb. The newly created most neutron-rich 39Na verifies the high precision of BNN prediction. Based on the promising results, future studies may apply the proposed methods to enhance the feasibility of creating a larger variety of ERIs in the newly commissioned FRIB factory, leading to more advanced experimental research.

The Background

Short-life, unstable, radioactive nuclei are exotic when they develop unusual structures. In order to facilitate the advancement of research, modern rare isotope beam (RIB) factories were set up to enhance the production of extremely rare isotopes (ERI) at or near drip lines. The ERI can be produced more effectively via a higher-level understanding on the projectile fragmentation reactions. As an effort to achieve that, this study performed multi-models predictions of cross-sections for ERIs in typical reactions using EPAX3, FRACS, BNN, and BNN + FRACS models, of which the BNN and BNN + FRACS models were newly developed massive learning models using Bayesian Neural Networks. The output of the study confirmed the feasibility to create a significantly large number of new isotopes in FRIB, leading to a richer knowledge base for nuclear structure, nuclear reaction, and nuclear astrophysics.

The SDG Impact

The new rare isotope factories provide unique technology to extend the boundaries on the chart of nucleus in experiments. This study shows how far from the β-stability line the ERI can reach within the lowest detecting limitations in the newly commissioned FRIB in Michigan State University, USA. By supporting future research related to nuclear science, its output is very well-aligned with UNSDG 9: Industries, Innovation & Infrastructure.


Graphical Abstract