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  • Open Access

Deep learning topological inference-guided Tcc+ pole parameter extraction

Julius B. Pagayon*, Klarence Tomas R. Cervantes†, and Denny Lane B. Sombillo‡

  • *Contact author: jbpagayon@up.edu.ph
  • †Contact author: krcervantes1@up.edu.ph
  • ‡Contact author: dbsombillo@up.edu.ph
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Phys. Rev. D 114, 014049 – Published 23 July, 2026

DOI: https://doi.org/10.1103/nr94-jzxj

Abstract

We perform a data-driven study of the doubly charmed tetraquark candidate Tcc+. 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 S-matrix and a complementary K-matrix parametrization, 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 Tcc+ is a shallow D0D*+ bound state in the second Riemann sheet of the complex plane.

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Phys. Rev. D 114, 014049– Published 23 July, 2026
  • Received 27 March 2026
  • Accepted 6 July 2026
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