Semideterministic and stochastic sampling of Feynman diagrams with 1/N_{f} expansions

We introduce a family of (semi)-bold-line series, assisted with 1/N_{f} expansions, with N_{f} being the number of fermion flavors. If there is no additional N_{f} cut, the series reduces to the random phase approximation series in the density-density channel, complementary to the particle-hole and...

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Main Author: Boyuan Shi
Format: Article
Language:English
Published: American Physical Society 2025-05-01
Series:Physical Review Research
Online Access:http://doi.org/10.1103/PhysRevResearch.7.023177
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author Boyuan Shi
author_facet Boyuan Shi
author_sort Boyuan Shi
collection DOAJ
description We introduce a family of (semi)-bold-line series, assisted with 1/N_{f} expansions, with N_{f} being the number of fermion flavors. If there is no additional N_{f} cut, the series reduces to the random phase approximation series in the density-density channel, complementary to the particle-hole and particle-particle channels introduced in Phys. Rev. B 102, 195122 (2020)2469-995010.1103/PhysRevB.102.195122. To address the very localized integrands in diagrammatic Monte Carlo, we introduced an innovative VEGAS-RG-MCMC sampling method, where we found a significant decrease of autocorrelation time without the usage of the state-of-the-art many-configuration MCMC (MCMCMC) method while the combination of both is also straightforward. We performed extensive benchmarks for density, energy, and pressure with the t-t^{′} SU(N_{f}) Hubbard model on a square lattice and honeycomb lattices over a wide range of numerical methods. For benchmark purposes, we also implement bare-U symmetry-broken perturbation series for the two-dimensional SU(2) Hubbard model on the honeycomb lattice, where we found encouraging results from weak to intermediate couplings.
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spelling doaj-art-e2885e34920a4e13a45f336af76ad8362025-08-20T03:13:30ZengAmerican Physical SocietyPhysical Review Research2643-15642025-05-017202317710.1103/PhysRevResearch.7.023177Semideterministic and stochastic sampling of Feynman diagrams with 1/N_{f} expansionsBoyuan ShiWe introduce a family of (semi)-bold-line series, assisted with 1/N_{f} expansions, with N_{f} being the number of fermion flavors. If there is no additional N_{f} cut, the series reduces to the random phase approximation series in the density-density channel, complementary to the particle-hole and particle-particle channels introduced in Phys. Rev. B 102, 195122 (2020)2469-995010.1103/PhysRevB.102.195122. To address the very localized integrands in diagrammatic Monte Carlo, we introduced an innovative VEGAS-RG-MCMC sampling method, where we found a significant decrease of autocorrelation time without the usage of the state-of-the-art many-configuration MCMC (MCMCMC) method while the combination of both is also straightforward. We performed extensive benchmarks for density, energy, and pressure with the t-t^{′} SU(N_{f}) Hubbard model on a square lattice and honeycomb lattices over a wide range of numerical methods. For benchmark purposes, we also implement bare-U symmetry-broken perturbation series for the two-dimensional SU(2) Hubbard model on the honeycomb lattice, where we found encouraging results from weak to intermediate couplings.http://doi.org/10.1103/PhysRevResearch.7.023177
spellingShingle Boyuan Shi
Semideterministic and stochastic sampling of Feynman diagrams with 1/N_{f} expansions
Physical Review Research
title Semideterministic and stochastic sampling of Feynman diagrams with 1/N_{f} expansions
title_full Semideterministic and stochastic sampling of Feynman diagrams with 1/N_{f} expansions
title_fullStr Semideterministic and stochastic sampling of Feynman diagrams with 1/N_{f} expansions
title_full_unstemmed Semideterministic and stochastic sampling of Feynman diagrams with 1/N_{f} expansions
title_short Semideterministic and stochastic sampling of Feynman diagrams with 1/N_{f} expansions
title_sort semideterministic and stochastic sampling of feynman diagrams with 1 n f expansions
url http://doi.org/10.1103/PhysRevResearch.7.023177
work_keys_str_mv AT boyuanshi semideterministicandstochasticsamplingoffeynmandiagramswith1nfexpansions