论文标题
一种机器学习方法，用于分类许多风味QCD的相变的分类
A machine learning approach to the classification of phase transitions in many flavor QCD
论文作者
论文摘要
归一化流是生成机器学习模型，仅使用给定的分布样本，可以有效地近似概率分布。该结构用于插入从QCD模拟中获得的手性冷凝物，并在HISQ作用中具有五个变性的夸克风味。从这个模型中，获得了手性冷凝物的概率分布作为晶格体积，夸克质量和量规耦合的函数的模型。使用该模型，可以对一阶和跨界区域进行分类，并且这些区域之间的边界可以用临界质量标记。将该模型扩展到具有可变数量口味数量的QCD的相变的研究。
Normalizing flows are generative machine learning models which can efficiently approximate probability distributions, using only given samples of a distribution. This architecture is used to interpolate the chiral condensate obtained from QCD simulations with five degenerate quark flavors in the HISQ action. From this a model for the probability distribution of the chiral condensate as function of lattice volume, quark mass and gauge coupling is obtained. Using the model, first order and crossover regions can be classified and the boundary between these regions can be marked by a critical mass. An extension of this model to studies of phase transitions in QCD with variable number of flavors is expected to be possible.
