This notebook tutorial demonstrates a method for sampling Boltzmann distributions of lattice field theories using a class of machine learning models known as normalizing flows. The ideas and approaches proposed in arXiv:1904.12072, arXiv:2002.02428, and arXiv:2003.06413 are reviewed and a concrete implementation of the framework is presented. We apply this framework to a lattice scalar field theory and to U(1) gauge theory, explicitly encoding gauge symmetries in the flow-based approach to the latter. This presentation is intended to be interactive and working with the attached Jupyter notebook is recommended.
@article{arxiv.2101.08176,
title = {Introduction to Normalizing Flows for Lattice Field Theory},
author = {Michael S. Albergo and Denis Boyda and Daniel C. Hackett and Gurtej Kanwar and Kyle Cranmer and Sébastien Racanière and Danilo Jimenez Rezende and Phiala E. Shanahan},
journal= {arXiv preprint arXiv:2101.08176},
year = {2021}
}
Comments
38 pages, 5 numbered figures, Jupyter notebook included as ancillary file