Flow-based sampling in the lattice Schwinger model at criticality

Recent results suggest that flow-based algorithms may provide efficient sampling of field distributions for lattice field theory applications, such as studies of quantum chromodynamics and the Schwinger model. In this work, we provide a numerical demonstration of robust flow-based sampling in the Sc...

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Published inarXiv.org
Main Authors Albergo, Michael S, Boyda, Denis, Cranmer, Kyle, Hackett, Daniel C, Kanwar, Gurtej, Racanière, Sébastien, Rezende, Danilo J, Romero-López, Fernando, Shanahan, Phiala E, Urban, Julian M
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 23.02.2022
Subjects
Algorithms
Computer Science - Learning
Fermions
Field theory
Physics - High Energy Physics - Lattice
Physics - Statistical Mechanics
Quantum chromodynamics
Robustness (mathematics)
Sampling
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Summary:Recent results suggest that flow-based algorithms may provide efficient sampling of field distributions for lattice field theory applications, such as studies of quantum chromodynamics and the Schwinger model. In this work, we provide a numerical demonstration of robust flow-based sampling in the Schwinger model at the critical value of the fermion mass. In contrast, at the same parameters, conventional methods fail to sample all parts of configuration space, leading to severely underestimated uncertainties.
Bibliography:MIT-CTP/5409
ISSN:2331-8422
DOI:10.48550/arxiv.2202.11712