摘要
在极端相对论重离子碰撞条件下,精确构建有限重子化学势μ_(B)区域的量子色动力学(Quantum Chromodynamics,QCD)物质状态方程(Equation of State,EoS)是当前高能核物理研究的核心难题之一。本研究提出一种基于深度学习的准部分子模型,通过构建三个深度神经网络,成功实现了零μ_(B)条件下QCD状态方程的高精度重建。同时,经深入分析四阶广义磁化率χ_(4)^(B)在不同温度和μ_(B)下的单调性行为,大致限定了QCD临界点可能存在的区间为(T,μ_(B))=((0.113±0.019)GeV,(0.634±0.11)GeV)。此外,对四阶累积量比R_(42)随碰撞能量√s_(NN)的依赖关系计算,其结果不仅与实验数据高度契合,还在√s_(NN)≈6 GeV附近发现了极为显著的涨落现象。这一深度学习的准部分子模型,为有限重子密度下QCD物质的热力学与输运性质研究提供了全新的自洽理论框架,其推导出的状态方程参数不仅可以直接应用于相对论重离子对撞机束流能量扫描计划中的流体动力学模拟,还为深入探索QCD相图结构以及寻找临界点提供新的研究方法。
[Background]Accurately constructing the Equation of State(EoS)for Quantum Chromodynamics(QCD)matter in the region with finite baryon chemical potential(μ_(B))is a central challenge in modern high-energy nuclear physics research.[Purpose]This study aims to address this challenge and explore the QCD phase diagram structure and locate the critical endpoint.[Methods]First,the study constructed three deep neural networks to achieve high-precision reconstruction of the QCD EoS at zeroμ_(B).Then,we analyzed the monotonic behavior of the fourth-order generalized susceptibilityχ_(4)^(B)at different temperatures T andμ_(B),and calculated the dependence of the fourth-order cumulant ratio R_(42) on collision energy √s_(NN).[Results]We have estimated the possible location of the QCD critical endpoint(CEP)as(T,μ_(B))=((0.113±0.019)GeV,(0.634±0.11)GeV).The results for the fourth-order cumulant ratio R_(42) not only match the experimental data well but also show significant fluctuation behavior near 6 GeV.[Conclusions]The deep-learning quasi-parton model provides a self-consistent theoretical framework for studying the thermodynamic and transport properties of QCD matter at finite baryon density.The obtained EoS parameters can be directly applied to hydrodynamic simulations for the beam energy scan program of the Relativistic Heavy Ion Collider(RHIC),offering a new research tool for exploring the QCD phase diagram structure and searching for CEP.
作者
李甫鹏
庞龙刚
秦广友
LI Fupeng;PANG Longgang;QIN Guangyou(Key Laboratory of Quark and Lepton Physics(MOE)&Institute of Particle Physics,Central China Normal University,Wuhan 430079,China;Artificial Intelligence and Computational Physics Research Center,Central China Normal University,Wuhan 430079,China)
出处
《核技术》
北大核心
2025年第5期74-83,共10页
Nuclear Techniques
基金
国家自然科学基金(No.12075098,No.12435009,No.12225503,No.11935007)
华中师范大学中央高校基本科研业务费项目资助。
关键词
有限重子化学势
QCD状态方程
深度学习
准部分子模型
QCD临界点
Finite baryon chemical potential
QCD equation of state
Deep learning
Quasi-particle model
QCD critical endpoint
