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arXiv · 2107.00734

Flow-based sampling for multimodal and extended-mode distributions in lattice field theory

Daniel C. Hackett·Chung-Chun Hsieh·Sahil Pontula·Michael S. Albergo·Denis Boyda·Jiunn-Wei Chen·Kai-Feng Chen·Kyle Cranmer·Gurtej Kanwar·Phiala E. Shanahan
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Abstract

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 training- and architecture-based methods to construct flow models for targets with multiple separated modes (i.e.~vacua) as well as targets with extended/continuous modes. We demonstrate the application of these methods to modeling two-dimensional real and complex scalar field theories in their symmetry-broken phases. In this context we investigate 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.

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Daniel C. Hackett, Chung-Chun Hsieh, Sahil Pontula, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan. 2021-07-01. Flow-based sampling for multimodal and extended-mode distributions in lattice field theory. https://arxiv.org/abs/2107.00734

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