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arXiv · 2101.08176

Introduction to Normalizing Flows for Lattice Field Theory

Michael S. Albergo·Denis Boyda·Daniel C. Hackett·Gurtej Kanwar·Kyle Cranmer·Sébastien Racanière·Danilo Jimenez Rezende·Phiala E. Shanahan
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Abstract

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.

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Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan. 2021-01-20. Introduction to Normalizing Flows for Lattice Field Theory. https://arxiv.org/abs/2101.08176

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