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

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 $D^0{D}^{\ast +}$ bound state in the second Riemann sheet of the complex plane.

20 pages, 12 figures