Hypernuclear cluster states of Unveiled through Neural Network-Driven Microscopic Calculation
Jiaqi Tian🇨🇳 · Mengjiao Lyu🇨🇳 · Zheng Cheng🇨🇳 · Masahiro Isaka🇯🇵 · Akinobu Dote🇯🇵 · Takayuki Myo🇯🇵 · Hisashi Horiuchi🇯🇵 · Hiroki Takemoto🇯🇵 · Niu Wan🇨🇳 · Qing Zhao🇨🇳
We investigate the hypernuclear cluster states of using a neural-network-driven microscopic model. We extend the Control Neural Networks (Ctrl.NN) method and systematically calculate the positive-parity spectrum of . By incorporating -shell excitations and parity-coupling effects into the hypernuclear system, we reveal structural changes, including clustering effects and new configurations such as isosceles-triangle and -- linear-chain structures. Furthermore, by comparing with experimental data, we identify that many peaks (6 and 8) can be interpreted as dominant states, which is consistent with shell-model predictions. Notably, based on our analysis of the excited states of , we propose possible candidates for previously unexplained or controversial experimental peaks.