AI-Driven Many-Body Frameworks

I have developed a unified theoretical framework that integrates Deep Learning with microscopic models to solve the quantum many-body problem.

• Methodology: I constructed the CB-Hyper-Brink model, which introduces complex generator coordinates to capture cluster-breaking effects, spin-orbit coupling, and high-momentum correlations within a fully antisymmetrized framework. To navigate the immense Hilbert space of non-orthogonal bases, I pioneered the Control Neural Network (Ctrl.NN) method, an AI agent that autonomously constructs and optimizes wave functions via physical variational principles.

• Applications: This approach has successfully described the structure of light hypernuclei, such as the rotational bands of \(^{9-11}_{\Lambda}\text{Be}\) isotopes and the parity-mixed spectroscopy of \(^{12}_{\Lambda}\text{B}\), revealing the interplay between cluster dynamics and \(\Lambda\)-induced core shrinkage.

Ab Initio Few-Body Hypernuclear Physics

Moving forward, I am expanding my research into ab initio calculations of few-body hypernuclear systems using the Gaussian Expansion Method (GEM).

• Goal: By leveraging the high precision of GEM in handling complicated strong interaction and exotic structures, I aim to rigorously solve 3- to 5-body hypernuclear systems and extend the framework to handle $A \ge 6$ systems. A core focus is modeling explicit $\Lambda - \Sigma$ coupling dynamics and engaging realistic baryonic interactions, which are crucial for interpreting binding energies and level spacings.

• Impact: This research provides distinct insights into baryonic matter. By bridging microscopic Hyperon-Nucleon interactions to observables, it deepens our quantitative understanding of the strong force in the strangeness sector and contributes to solving the Hyperon Puzzle in neutron stars.