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  • Letter
  • Open Access

Control variates for lattice field theory

Tanmoy Bhattacharya1,*, Scott Lawrence2,†, and Jun-Sik Yoo1,‡

  • 1Group T-2, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
  • 2Department of Physics, University of Colorado, Boulder, Colorado 80309, USA

  • *tanmoy@lanl.gov
  • †scott.lawrence-1@colorado.edu
  • ‡junsik@lanl.gov

Phys. Rev. D 109, L031505 – Published 23 February, 2024

DOI: https://doi.org/10.1103/PhysRevD.109.L031505

Abstract

In most lattice field theories, correlators are plagued by a signal-to-noise problem of exponential difficulty in the time separation. We propose a method for improving the signal-to-noise ratio, in which control variates are systematically constructed from lattice Schwinger-Dyson relations. The method is demonstrated on various two-dimensional lattices in scalar field theory, and a strategy for scaling to larger systems is explored.

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References (19)

  1. A. S. Kronfeld, T. Bhattacharya, T. Blum, N. H. Christ, C. DeTar et al. (USQCD Collaboration), Lattice QCD and particle physics, arXiv:2207.07641.
  2. Y.-C. Jang, R. Gupta, B. Yoon, and T. Bhattacharya, Axial vector form factors from lattice QCD that satisfy the PCAC relation, Phys. Rev. Lett. 124, 072002 (2020).
  3. G. Parisi, The strategy for computing the hadronic mass spectrum, Phys. Rep. 103, 203 (1984).
  4. G. P. Lepage, The analysis of algorithms for lattice field theory, in Theoretical Advanced Study Institute in Elementary Particle Physics (World Scientific, Singapore, 1990).
  5. Y. Aoki, T. Blum, G. Colangelo, S. Collins, M. Della Morte et al. (Flavour Lattice Averaging Group (FLAG) Collaboration), FLAG review 2021, Eur. Phys. J. C 82, 869 (2022).
  6. C. Aubin, T. Blum, C. Tu, M. Golterman, C. Jung, and S. Peris, Light quark vacuum polarization at the physical point and contribution to the muon g−2, Phys. Rev. D 101, 014503 (2020).
  7. T. Blum, T. Izubuchi, and E. Shintani, New class of variance-reduction techniques using lattice symmetries, Phys. Rev. D 88, 094503 (2013).
  8. B. Yoon, T. Bhattacharya, and R. Gupta, Machine learning estimators for lattice QCD observables, Phys. Rev. D 100, 014504 (2019).
  9. W. Detmold, G. Kanwar, M. L. Wagman, and N. C. Warrington, Path integral contour deformations for noisy observables, Phys. Rev. D 102, 014514 (2020).
  10. G. Kanwar, A. Lovato, N. Rocco, and M. Wagman, Mitigating Green’s function Monte Carlo signal-to-noise problems using contour deformations, arXiv:2304.03229.
  11. A. Alexandru, G. Basar, P. F. Bedaque, and N. C. Warrington, Complex paths around the sign problem, Rev. Mod. Phys. 94, 015006 (2022).
  12. S. Lawrence, Perturbative removal of a sign problem, Phys. Rev. D 102, 094504 (2020).
  13. S. Lawrence and Y. Yamauchi, Deep learning of fermion sign fluctuations, Phys. Rev. D 107, 114505 (2023).
  14. L. Fernandez and V. Martin-Mayor, Mean-value identities as an opportunity for Monte Carlo error reduction, Phys. Rev. E 79, 051109 (2009).
  15. M. Weigel and W. Janke, Error estimation and reduction with cross correlations, Phys. Rev. E 81, 066701 (2010).
  16. M. E. Peskin and D. V. Schroeder, An Introduction to Quantum Field Theory (Addison-Wesley, Reading, USA, 1995).
  17. A. Bazavov, C. Bernard, C. DeTar, W. Freeman, S. Gottlieb et al. (MILC Collaboration), Lattice QCD ensembles with four flavors of highly improved staggered quarks, Phys. Rev. D 87, 054505 (2013).
  18. A. Beck and M. Teboulle, A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM J. Imaging Sci. 2, 183 (2009).
  19. T. Bhattacharya, S. Lawrence, and J.-S. Yoo (2023), https://gitlab.com/s.lawrence/latticecv (source on Gitlab) (2023).

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