4.7 Article

Flow-based sampling in the lattice Schwinger model at criticality

Journal

PHYSICAL REVIEW D
Volume 106, Issue 1, Pages -

Publisher

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevD.106.014514

Keywords

-

Funding

  1. U.S. Department of Energy, Office of Science, Office of Nuclear Physics [DE-SC0011090]
  2. National Science Foundation under EAGER [2035015]
  3. U.S. DOE Early Career [DE-SC0021006]
  4. NEC Research Award
  5. Carl G. and Shirley Sontheimer Research Fund
  6. Schweizerischer Nationalfonds
  7. National Science Foundation [PHY-2141336]
  8. U.S. Department of Energy Office of Science User Facility [DE-AC02-06CH11357]
  9. Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy [EXC 2181/1-390900948]
  10. Collaborative Research Centre [SFB 1225]
  11. U.S. National Science Foundation [PHY-2019786]
  12. DOE Office of Science User Facility [DEAC02-06CH11357]
  13. Division Of Physics
  14. Direct For Mathematical & Physical Scien [2035015] Funding Source: National Science Foundation
  15. U.S. Department of Energy (DOE) [DE-SC0021006] Funding Source: U.S. Department of Energy (DOE)

Ask authors/readers for more resources

Recent results suggest that flow-based algorithms are efficient for sampling field distributions in lattice field theory applications. This study provides a numerical demonstration on the robustness of flow-based sampling in the Schwinger model at the critical fermion mass value. Conventional methods, on the other hand, fail to sample all parts of configuration space and lead to significantly underestimated uncertainties.
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.

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