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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.17763 |
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| _version_ | 1866918395440529408 |
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| author | Pagayon, Julius B. Cervantes, Klarence Tomas R. Sombillo, Denny Lane B. |
| author_facet | Pagayon, Julius B. Cervantes, Klarence Tomas R. Sombillo, Denny Lane B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_17763 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep learning topological inference-guided $T_{cc}^{+}$ pole parameter extraction Pagayon, Julius B. Cervantes, Klarence Tomas R. Sombillo, Denny Lane B. High Energy Physics - Phenomenology 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. |
| title | Deep learning topological inference-guided $T_{cc}^{+}$ pole parameter extraction |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2603.17763 |