Hypernuclear cluster states of $_\Lambda^{12}\rm{B}$ Unveiled through Neural Network-Driven Microscopic Calculation

TL;DR

This paper uses an advanced neural network-based microscopic model to explore and identify complex cluster states in $_\Lambda^{12}\mathrm{B}$ hypernuclei, revealing new configurations and matching experimental data.

Contribution

It extends the Control Neural Networks method to systematically analyze $_\Lambda^{12}\mathrm{B}$, incorporating excitations and parity effects to uncover novel structural states and interpret experimental peaks.

Findings

Identification of clustering effects and new configurations such as linear chains.

Many peaks correspond to $p_\Lambda$ dominant states consistent with shell-model predictions.

Proposed candidates for previously unexplained experimental peaks.

Abstract

We investigate the hypernuclear cluster states of $_\Lambda^{12}\mathrm{B}$ using a neural-network-driven microscopic model. We extend the Control Neural Networks (Ctrl.NN) method and systematically calculate the positive-parity spectrum of $_\Lambda^{12}\mathrm{B}$. By incorporating $sd$-shell excitations and parity-coupling effects into the $_\Lambda^{12}\mathrm{B}$ hypernuclear system, we reveal structural changes, including clustering effects and new configurations such as isosceles-triangle and $\alpha$-$t$-$\alpha$ linear-chain structures. Furthermore, by comparing with experimental data, we identify that many peaks ($\#$6 and $\#$8) can be interpreted as $p_\Lambda$ dominant states, which is consistent with shell-model predictions. Notably, based on our analysis of the excited states of $_\Lambda^{12}\mathrm{B}$, we propose possible candidates for previously unexplained or controversial experimental peaks.