2021/12/02 by Sam Foreman, Foreman, Sam, Xiao-Yong Jin +3
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Lattice (hep-lat) #Machine Learning (cs.LG) #cs.LG #hep-lat
paper · pdf · doi:10.48550/arxiv.2112.01582
10 pages, 12 figures, presented at the 38th International Symposium on Lattice Field Theory, LATTICE2021 26th-30th July, 2021, Zoom/Gather @ Massachusetts Institute of Technology
arxiv created 2022/01/14 · arxiv updated 2022/01/17
We introduce LeapfrogLayers, an invertible neural network architecture that can be trained to efficiently sample the topology of a 2D U(1) lattice gauge theory. We show an improvement in the integrated autocorrelation time of the topological charge when compared with traditional HMC, and look at how different quantities transform under our model. Our implementation is open source, and is publicly available on github at https://github.com/saforem2/l2hmc-qcd.