Deep learning topological inference-guided $T_{cc}^{+}$ pole parameter extraction

TL;DR

This paper uses deep learning and complex analysis techniques to identify and characterize the pole structure of the $T_{cc}^+$ tetraquark candidate, concluding it is a shallow bound state.

Contribution

It introduces a data-driven neural network approach combined with model-independent and dynamical analyses for pole extraction in exotic hadron states.

Findings

Identifies a dominant pole topology on the $[bt]$ Riemann sheet.

Provides a robust pole position and trajectory analysis.

Concludes $T_{cc}^+$ is a shallow $D^0D^{*+}$ bound state.

Abstract

We perform a data-driven study of the doubly charmed tetraquark candidate $T_{cc}^+$. An ensemble of deep neural network classifiers, trained on synthetic amplitudes with controlled analytic structures, identifies a dominant pole topology characterized by an isolated pole on the $[bt]$ Riemann sheet which is robust against left-hand cut effects. A subsequent pole parameter extraction was performed via the uniformized $\mathcal{S}$-matrix and a complementary $\mathcal{K}$-matrix parameterization, which respectively provides a model-independent baseline and dynamical insight on the pole position and trajectory of the resonant state. Using this two-pronged approach, we submit that the $T_{cc}^{+}$ is a shallow $D^0D^{*+}$ bound state in the second Riemann sheet of the complex plane.