AI-Assisted analysis of $^{28}$Si$^*$ $\rightarrow$ 7$\alpha$ break-up data

TL;DR

This paper introduces an AI-based machine learning approach using Gaussian Mixture Models to analyze experimental data on $^{28}$Si$^*$ break-up, providing evidence for exotic toroidal resonances in mid-weight alpha-conjugate nuclei.

Contribution

The study develops a novel AI and GMM-based analysis method combined with the H$$C model to identify potential toroidal states in $^{28}$Si$^*$ data, advancing nuclear structure research.

Findings

Evidence of structures consistent with toroidal states in $^{28}$Si$^*$ data.

AI-based analysis reveals underlying patterns near theoretical predictions.

Supports the existence of exotic high-angular-momentum resonances.

Abstract

Mid-weight $\alpha$-conjugate nuclei are predicted to possess exotic toroid-like resonances with high angular momenta. The search for these states in $^{28}$Si$^*$ is the main point of two published experimental investigations of the peripheral $^{28}$Si + $^{12}$C reaction by Cao and collaborators and by Hannaman and collaborators. In this work, we develop a novel Artificial Intelligence (AI)-based machine learning method utilizing the Gaussian Mixture Model (GMM) to analyze available experimental and theoretical data. We additionally study the reaction with the Hybrid $\alpha$-Cluster (H$\alpha$C) model. In all the examined data our results suggest the presence of underlying structure which is close to that predicted for toroidal states.