Exploring the QCD phase transition in relativistic heavy-ion collisions with fluctuations of conserved charges and machine learning

极端密度和温度下强相互作用物质的性质对于我们理解物质的基本成分很重要。高密度和中等温度下的状态方程是核物质最重要的性质，也与最近观测到的中子星合并等宇宙事件有关。在零重子化学势下，晶格QCD建立了光滑的交叉，与相对论重离子对撞机(RHIC)和大型强子对撞机(LHC)的超相对论重离子反应的实验数据相一致。在有限的净重子密度下，符号问题阻碍了第一原理的计算，人们只能求助于对强相互作用物质相图的实验探索。守恒电荷的涨落对于QCD相图中的一级相变或临界终点是很有希望的。在这项提议中，我们计划开展一项密切的实验-理论合作，以确定夸克-胶子等离子体和强子气体之间相变的特征。一项关键的任务是仔细研究守恒电荷的高阶矩的测量结果，并量化背景效应。在专门的输运和流体力学计算中，将详细研究多粒子效率、运动学切割、守恒定律和重子输运。受中心性选择影响的成交量波动也将得到解决。决定系统中净重子密度的重子停止将在强子弦方法中进行研究。其目标是通过提取与关键终点的物理相关的波动量来澄清现有测量中的模糊性，并为未来的实验提供指导。此外，还将应用机器学习技术来识别新的敏感观测对象。不同状态方程的混合计算将被用来训练一个深度卷积神经网络，以从模拟和真实的实验数据中识别Eos。为此，基于GPU的高效三维粘性流体力学算法CLVisc将被推广到有限净重子密度下。通过结合中国和德国申请者的互补专业知识，拟议的勘探将为更好地理解QCD相图提供新的途径。

Properties of strongly interacting matter at extreme densities and temperatures are important for our understanding of the fundamental constituents of matter. The equation of state at high densities and moderate temperatures is the most important property of nuclear matter that is also relevant in cosmic events such as the recently observed neutron star mergers. At zero baryon chemical potential, a smooth cross-over has been established by lattice QCD and is consistent with experimental data in ultra-relativistic heavy-ion reactions at RHIC (Relativistic Heavy Ion Collider) and LHC (Large Hadron Collider). At finite net baryon density the sign problem prevents first principle calculations and one can only resort to an experimental exploration of the phase diagram of strongly interacting matter. Fluctuations of conserved charges are promising observables for a first order phase transition or critical endpoint in the QCD phase diagram. In this proposal, we plan to carry out a close experiment-theory collaboration to identify signatures of the phase transition between the quark-gluon plasma and the hadron gas. One critical task is to scrutinize measurements of higher moments of conserved charges and quantify background effects. Within dedicated transport and hydrodynamic calculations, multi-particle efficiencies, kinematic cuts, conservation laws and baryon transport are going to be investigated in detail. Volume fluctuations affected by the centrality selection are going to be addressed as well. The baryon stopping that determines the amount of net baryon density in the system will be investigated in a hadron-string approach. The goal is to clarify ambiguities in existing measurements by extracting the amount of the fluctuations associated with the physics of the critical endpoint and provide guidance for future experiments. In addition, machine learning techniques will be applied to identify new sensitive observables. Hybrid calculations with different equations of state will be used to train a deep convolutional neural network to identify the EoS from simulated and real experimental data. For this purpose, the highly efficient GPU based algorithm CLVisc for 3-dimensional viscous hydrodynamics is going to be extended to finite net baryon densities. By combining the complementary expertise of the Chinese and German applicants, the proposed exploration will provide insights on new routes to a better understanding of the QCD phase diagram.