- Letter
- Open Access
Control variates for lattice field theory
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.
Physics Subject Headings (PhySH)
Article Text
References (19)
- A. S. Kronfeld, T. Bhattacharya, T. Blum, N. H. Christ, C. DeTar et al. (USQCD Collaboration), Lattice QCD and particle physics, arXiv:2207.07641.
- 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).
- G. Parisi, The strategy for computing the hadronic mass spectrum, Phys. Rep. 103, 203 (1984).
- G. P. Lepage, The analysis of algorithms for lattice field theory, in Theoretical Advanced Study Institute in Elementary Particle Physics (World Scientific, Singapore, 1990).
- 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).
- 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 , Phys. Rev. D 101, 014503 (2020).
- T. Blum, T. Izubuchi, and E. Shintani, New class of variance-reduction techniques using lattice symmetries, Phys. Rev. D 88, 094503 (2013).
- B. Yoon, T. Bhattacharya, and R. Gupta, Machine learning estimators for lattice QCD observables, Phys. Rev. D 100, 014504 (2019).
- W. Detmold, G. Kanwar, M. L. Wagman, and N. C. Warrington, Path integral contour deformations for noisy observables, Phys. Rev. D 102, 014514 (2020).
- 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.
- A. Alexandru, G. Basar, P. F. Bedaque, and N. C. Warrington, Complex paths around the sign problem, Rev. Mod. Phys. 94, 015006 (2022).
- S. Lawrence, Perturbative removal of a sign problem, Phys. Rev. D 102, 094504 (2020).
- S. Lawrence and Y. Yamauchi, Deep learning of fermion sign fluctuations, Phys. Rev. D 107, 114505 (2023).
- L. Fernandez and V. Martin-Mayor, Mean-value identities as an opportunity for Monte Carlo error reduction, Phys. Rev. E 79, 051109 (2009).
- M. Weigel and W. Janke, Error estimation and reduction with cross correlations, Phys. Rev. E 81, 066701 (2010).
- M. E. Peskin and D. V. Schroeder, An Introduction to Quantum Field Theory (Addison-Wesley, Reading, USA, 1995).
- 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).
- A. Beck and M. Teboulle, A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM J. Imaging Sci. 2, 183 (2009).
- T. Bhattacharya, S. Lawrence, and J.-S. Yoo (2023), https://gitlab.com/s.lawrence/latticecv (source on Gitlab) (2023).