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  • Open Access

Melting of heavy quarkonia in QGP using deep neural networks

Mohammad Yousuf Jamal1,*, Fu-Peng Li1,2,3,4,†, Long-Gang Pang1,2,‡, and Guang-You Qin1,2,§

  • 1Key Laboratory of Quark and Lepton Physics (MOE) & Institute of Particle Physics, Central China Normal University, Wuhan 430079, China
  • 2Artificial Intelligence and Computational Physics Research Center, Central China Normal University, Wuhan 430079, China
  • 3Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) & Institute of Modern Physics, Fudan University, Shanghai 200433, China
  • 4Shanghai Research Center for Theoretical Nuclear Physics, NSFC and Fudan University, Shanghai 200438, China

  • *Contact author: yousufjml5@gmail.com
  • †Contact author: fpli@fudan.edu.cn
  • ‡Contact author: lgpang@ccnu.edu.cn
  • §Contact author: guangyou.qin@ccnu.edu.cn
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Phys. Rev. C 113, 034915 – Published 18 March, 2026

DOI: https://doi.org/10.1103/z3kh-zr1t

Abstract

Machine learning techniques have emerged as powerful tools for tackling nonperturbative challenges in quantum chromodynamics. In this study, we introduce a data-driven framework employing deep neural networks to systematically predict the temperature-dependent behavior of the screening mass mD(T) and the strong coupling constant αs(T) within a quark-gluon plasma medium. These medium-sensitive quantities are subsequently employed to compute the thermal widths Γn(T) and binding energies EB(T) of heavy quarkonia states, specifically charmonia and bottomonia, by numerically solving the Schrödinger equation with medium-modified heavy quark potentials. To estimate the dissociation temperatures Td of various quarkonia states, we employ two complementary dissociation criteria: the conventional one, where 2EB(Td)=Γn(Td), and an additional lower bound criterion defined by EB(Td)=3Td. This dual-criterion approach provides a more constrained and physically motivated estimate of the temperature range over which quarkonia states dissolve in the quark-gluon plasma (QGP) environment. Our machine learning-enhanced predictions show excellent agreement with available lattice quantum chromodynamics results, especially for the ground states Υ(1S) and J/ψ, and offer new perspectives on the sequential suppression pattern detected in relativistic heavy-ion collision experiments. Overall, this work advances the quantitative description of quarkonium suppression and demonstrates the prospect of modern machine learning methods to bridge theoretical predictions and experimental observations, thereby contributing significantly to QGP tomography.

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Phys. Rev. C 113, 034915– Published 18 March, 2026
  • Received 23 September 2025
  • Revised 23 December 2025
  • Accepted 20 February 2026
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