We investigate the cluster-breaking effect and spatial distribution of negative-parity states in the ${}_{\mathrm{\Lambda }}{}^{12}\mathrm{B}$ hypernucleus using the Hyper-Brink model with cluster breaking (CB-Hyper-Brink) optimized via control neural network (Ctrl.NN). The results demonstrate that the inclusion of cluster breaking is essential for accurately reproducing the observed low-lying energy levels and for making reliable predictions of the Hoyle-analog state $1_4^{-}$ in ${}_{\mathrm{\Lambda }}{}^{12}\mathrm{B}$. Cluster breaking manifests as strong spin-orbit correlations and the dissolution of ideal cluster configurations, as revealed by the analysis of one-body spin-orbit-operator expectation values and the spatial overlap with projected cluster bases. The interplay between short-range repulsion and intermediate-range attraction in the $\mathrm{\Lambda }N$ interaction induces the cluster reconfiguration effect, which is characterized by the coexistence of $\mathrm{\Lambda }\text{−}\alpha$ and $\mathrm{\Lambda }$-triton correlations; this reconfiguration effect leads to a modest stabilization and shrinkage of cluster structures. The variation in electric quadrupole transition strengths, $B\left(E2\right)$, between the ground and Hoyle-analog states, serves as a sensitive probe for the degree of cluster breaking, providing direct evidence for its physical relevance. These findings highlight the crucial role of cluster breaking in characterizing the hypernuclear structure and offer a comprehensive framework for understanding the interplay between clustering and shell-model dynamics in hypernuclei.

Close

PrevNext

View figure in article

• Hypernuclei & mesic nuclei
• Nucleon distribution
• 6 ≤ A ≤ 19
• Artificial neural networks