Papers › Self-learning Monte-Carlo for non-abelian gauge theory with dynamical fermions
Self-learning Monte-Carlo for non-abelian gauge theory with dynamical fermions
Yuki Nagai, Akinori Tanaka, Akio Tomiya
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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 m̂ = 0.5 is likely in the crossover regime in the Colombia plot.
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