发表机构
Central China Normal University; Fudan University; Shanghai Research Center for Theoretical Nuclear Physics（华中师范大学; 复旦大学; 上海理论核物理研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文利用机器学习从格点数据提取德拜质量，改进复势模型，计算底夸克偶素在夸克-胶子等离子体中的抑制，与实验数据更吻合，尤其对激发态。
AI 中文摘要
受数据驱动方法近期进展的启发，我们引入一个从格点输入中提取的机器学习（ML）推断的德拜质量，并专门将其用于复值重夸克肯特州立大学（KSU）势中。所得复势被用于在量子轨迹（QTraj）框架内求解实时薛定谔方程，以描述夸克-胶子等离子体中底夸克偶素的演化。随后，我们计算了在√sNN = 5.02 TeV的Pb-Pb碰撞中底夸克偶素Υ(1S)、Υ(2S)和Υ(3S)态的核修正因子和双比率。我们将机器学习诱导的结果与原始KSU模型的结果以及ALICE、ATLAS和CMS合作组的实验测量进行比较。我们发现，机器学习推断的德拜质量改善了与数据的一致性，特别是对于激发态，这凸显了机器学习在模拟介质中QCD效应方面的实用性。
英文摘要
Motivated by recent progress in data-driven approaches, we introduce a machine-learning (ML)-informed Debye mass, extracted from lattice-informed inputs, exclusively in the complex-valued heavy-quark Kent State University (KSU) potential. The resulting complex potential is used to solve the real-time Schrödinger equation within the quantum trajectories (QTraj) framework for the evolution of bottomonium in the quark-gluon plasma. We then compute the nuclear modification factors and double ratios for bottomonium $Υ(1S)$, $Υ(2S)$, and $Υ(3S)$ states in Pb-Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV. We compare our ML-induced results with those from the original KSU model and with experimental measurements from ALICE, ATLAS, and CMS collaborations. We find that the machine-learned Debye mass leads to improved agreement with data, particularly for excited states, highlighting the utility of machine learning in modeling in-medium QCD effects.
Comments14 pages, 8 figures; revised and reformatted manuscript with updated references
