Multiple-models prediction for light neutron-rich isotopes cross section by $Q_g$ systematics in $^{40}$Ar projectile fragmentation reactions

TL;DR

This study evaluates the predictive accuracy of multiple models, including Bayesian neural networks, for light neutron-rich isotope cross sections using $Q_g$ systematics in projectile fragmentation, revealing insights into nuclear shell closures.

Contribution

It introduces a multi-model approach combining FRACS and BNN models to predict isotope cross sections near drip lines, enhancing prediction reliability.

Findings

Models are generally consistent in predictions.

Evidence for shell closure at N=16 in fluorine and neon.

Disappearance of N=20 magic number in certain isotopes.

Abstract

Precise predictions for nuclei near drip lines are crucial for experiments in new generation of rare isotope facilities. A multi-models investigation of the $Q_g$ systematics for fragments production cross sections, with $Q_g$ defined as the difference of mass excess (ME) between the projectile ($Z_{p}, A_{p}$) and the fragment ($Z_{f}, A_{f}$) nuclei $Q_{g}=ME(Z_{p}, A_{p})-ME(Z_{f}, A_{f})$, has been performed to verify the model prediction abilities for light neutron-rich isotopes in measured $^{40}$Ar + $^9$Be projectile fragmentation reactions from 57$A$ MeV to 1$A$ GeV. The models used are the FRACS parametrizations and the newly developed Bayesian neural networks (BNN) model. %method The results show that FRACS, BNN, and $Q_g$ extrapolations are generally consistent, except for fragments near the nuclear mass of the projectile. Additionally, both measured data and model extrapolations provide evidence for a shell closure at $N=$ 16 in fluorine and neon, as well as the disappearance of the traditional magic number $N=$ 20 in neon, sodium and magnesium.