Flow-based sampling for multimodal distributions in lattice field theory

Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory. In this paper, we present a set of methods to construct flow models for targets with multiple separated modes (i.e. theories...

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Bibliographic Details
Published inarXiv.org
Main Authors Hackett, Daniel C, Chung-Chun, Hsieh, Albergo, Michael S, Boyda, Denis, Chen, Jiunn-Wei, Kai-Feng, Chen, Cranmer, Kyle, Kanwar, Gurtej, Shanahan, Phiala E
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.07.2021
Subjects
Algorithms
Field theory
Lattice vibration
Samplers
Sampling
Scalars
Two dimensional models
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Summary:Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory. In this paper, we present a set of methods to construct flow models for targets with multiple separated modes (i.e. theories with multiple vacua). We demonstrate the application of these methods to modeling two-dimensional real scalar field theory in its symmetry-broken phase. In this context we investigate the performance of different flow-based sampling algorithms, including a composite sampling algorithm where flow-based proposals are occasionally augmented by applying updates using traditional algorithms like HMC.
ISSN:2331-8422