LeapfrogLayers: A Trainable Framework for Effective Topological Sampling
High Energy Physics - Lattice
2022-01-17 v2 Machine Learning
Abstract
We introduce LeapfrogLayers, an invertible neural network architecture that can be trained to efficiently sample the topology of a 2D 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.
Cite
@article{arxiv.2112.01582,
title = {LeapfrogLayers: A Trainable Framework for Effective Topological Sampling},
author = {Sam Foreman and Xiao-Yong Jin and James C. Osborn},
journal= {arXiv preprint arXiv:2112.01582},
year = {2022}
}
Comments
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