Mapping low-lying states and B(E2;01+→21+) in even-even nuclei with machine learning

Abstract

A machine-learning algorithm, Light Gradient Boosting Machine, was applied for the first time to investigate the fundamental experimental observables in even-even nuclei over the Segrè chart. Specifically, we focused on the excitation energies of the 21+ and 41+ states, and the reduced electric quadrupole transition probability B(E2;01+→21+). Present obtained results well reproduced experimental data within an accuracy of 1.07, 1.05, and 1.14 times for the 21+ and 41+ states as well as B(E2;01+→21+), respectively, being significantly precise than the results from any state-of-the-art nuclear models and from any machine-learning-based approaches. The predictive capability of our machine learning methodology was further validated using 17 newly measured data points which were not used in the training set. Taking O, Ca, Sn, and Pb isotopes as examples, it has been found that our methodology precisely captures both the isotopic trend and absolute values, surpassing all theoretical models hitherto. Our findings reveal the double-magic nature of 100Sn and the disappearance of the N=20 shell in 28O.

Keywords

• Shell evolution
• B(E2;01+→21+)
• Low-lying states
• Machine learning