Flow-based sampling for multimodal distributions in lattice field theory

• URL: http://arxiv.org/abs/2107.00734v1
• Date: Thu, 1 Jul 2021 20:22:10 GMT
• Title: Flow-based sampling for multimodal distributions in lattice field theory
• Authors: Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, and Phiala E. Shanahan
• Abstract summary: We present a set of methods to construct flow models for targets with multiple separated modes. We demonstrate the application of these methods to modeling two-dimensional real scalar field theory.
• Score: 7.0631812650826085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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