Self-learning Monte Carlo for non-Abelian gauge theory with dynamical fermions
Self-learning Monte Carlo for non-Abelian gauge theory with dynamical fermions
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DOI：
10.1103/physrevd.107.054501
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发表时间：
2020-10
影响因子：
5
通讯作者：
Y. Nagai;A. Tanaka;A. Tomiya
中科院分区：
文献类型：
--
作者：
Y. Nagai;A. Tanaka;A. Tomiya
本文发展了四维非阿贝尔规范理论的自学习蒙特-卡罗（SLMC）算法来解决格点QCD中的自相关问题。在零温度和有限温度下，我们分别在HMC和SLMC中模拟了动力学交错费米子和格构规范作用，以检验SLMC的有效性。我们证实SLMC可以减少非阿贝尔规范理论中的自相关时间，并再现了HMC的结果。对于有限的温度运行，我们确认，SLMC再现正确的结果与HMC，包括高阶矩的Polyakov循环和手性凝聚。此外，我们的有限温度计算表明，四个风味QC${}_2$D与$\hat{m} = 0.5$可能在交叉制度在哥伦比亚的情节。
In this paper, we develop the self-learning Monte-Carlo (SLMC) algorithm for non-abelian gauge theory with dynamical fermions in four dimensions to resolve the autocorrelation problem in lattice QCD. We perform simulations with the dynamical staggered fermions and plaquette gauge action by both in HMC and SLMC for zero and finite temperature to examine the validity of SLMC. We confirm that SLMC can reduce autocorrelation time in non-abelian gauge theory and reproduces results from HMC. For finite temperature runs, we confirm that SLMC reproduces correct results with HMC, including higher-order moments of the Polyakov loop and the chiral condensate. Besides, our finite temperature calculations indicate that four flavor QC${}_2$D with $\hat{m} = 0.5$ is likely in the crossover regime in the Colombia plot.