论文标题
从手性冷凝物中衍生出改善全息QCD的DILATON潜力
Deriving dilaton potential in improved holographic QCD from chiral condensate
论文作者
论文摘要
我们从手性冷凝物的QCD晶格数据中得出了改进的全息QCD（IHQCD）中DILATON电位的明确形式。这建立了QCD的数据驱动全息模型 - 机器学习全息QCD。该建模包括解决反问题的两个步骤。第一个反问题是找到与边界处的晶格QCD仿真数据一致的新兴散装几何形状。我们通过改进神经普通微分方程（一种机器学习技术）来解决这个问题。第二个反问题是带有具有dilaton电位的大量重力作用，以便其解决方案是新兴的散装几何形状。我们在非零温度下解决此问题，并得出DILATON电位的明确形式。 DILATON电位决定了体积作用，即爱因斯坦 - 迪拉顿系统，因此我们从QCD手性冷凝物数据中以全息系统得出大量系统。模型的有用性显示在弦线断裂距离的预测示例中，其值被认为与另一个晶格QCD数据一致。
We derive an explicit form of the dilaton potential in improved holographic QCD (IHQCD) from the QCD lattice data of the chiral condensate as a function of the quark mass. This establishes a data-driven holographic modeling of QCD -- machine learning holographic QCD. The modeling consists of two steps for solving inverse problems. The first inverse problem is to find the emergent bulk geometry consistent with the lattice QCD simulation data at the boundary. We solve this problem with the refinement of neural ordinary differential equation, a machine learning technique. The second inverse problem is to derive a bulk gravity action with a dilaton potential such that its solution is the emergent bulk geometry. We solve this problem at non-zero temperature, and derive the explicit form of the dilaton potential. The dilaton potential determines the bulk action, the Einstein-dilaton system, thus we derive holographically the bulk system from the QCD chiral condensate data. The usefulness of the model is shown in the example of the prediction of the string breaking distance, whose value is found to be consistent with another lattice QCD data.
