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MLMC: Machine Learning Monte Carlo for Lattice Gauge Theory

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arxiv 2312.08936 v2 pith:7ENAXDJZ submitted 2023-12-14 hep-lat

Sam Foreman , Xiao-Yong Jin , James C. Osborn This is my paper · ORCID
classification hep-lat
keywords gaugeconfigurationsconsiderlatticesamplingtheoryavailablecarlo
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abstract

We present a trainable framework for efficiently generating gauge configurations, and discuss ongoing work in this direction. In particular, we consider the problem of sampling configurations from a 4D $SU(3)$ lattice gauge theory, and consider a generalized leapfrog integrator in the molecular dynamics update that can be trained to improve sampling efficiency. Code is available online at https://github.com/saforem2/l2hmc-qcd.

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