📝 SummarXiv

Abstract

Machine learning techniques have emerged as powerful tools for tackling non-perturbative 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 $m_D(T)$ and the strong coupling constant $\alpha_s(T)$ within a quark-gluon plasma medium. These medium-sensitive quantities are subsequently employed to compute the thermal widths $\Gamma_{\text{n}}(T)$ and binding energies $E_B(T)$ of heavy quarkonia states, specifically charmonia and bottomonia, by numerically solving the Schr\"odinger equation with medium-modified heavy quark potentials. To estimate the dissociation temperatures $T_d$ of various quarkonia states, we employ two complementary dissociation criteria: the conventional one, where $2E_B(T_d) = \Gamma_{\text{n}}(T_d)$, and an additional lower bound criterion defined by $E_B(T) = 3T$. This dual-criterion approach provides a more constrained and physically motivated estimate of the temperature range over which quarkonia states dissolve in the QGP environment. Our machine learning-enhanced predictions show excellent agreement with available lattice QCD results, especially for the ground states $\Upsilon(1S)$ and $J/\psi$, 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.