Signal-to-noise improvement through neural network contour deformations for 3D $SU(2)$ lattice gauge theory
Conference
·
OSTI ID:2000987
- MIT, Cambridge, CTP; IAIFI, Cambridge
- U. Bern, AEC; Bern U.
- Fermilab
Complex contour deformations of the path integral have been demonstrated to significantly improve the signal-to-noise ratio of observables in previous studies of two-dimensional gauge theories with open boundary conditions. In this work, new developments based on gauge fixing and a neural network definition of the deformation are introduced, which enable an effective application to theories in higher dimensions and with generic boundary conditions. Improvements of the signal-to-noise ratio by up to three orders of magnitude for Wilson loop measurements are shown in $SU(2)$ lattice gauge theory in three spacetime dimensions.
- Research Organization:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- DOE Contract Number:
- AC02-07CH11359
- OSTI ID:
- 2000987
- Report Number(s):
- FERMILAB-CONF-23-485-T; arXiv:2309.00600; oai:inspirehep.net:2692810
- Country of Publication:
- United States
- Language:
- English
Similar Records
Path integral contour deformations for observables in
gauge theory
Mitigating Green's function Monte Carlo signal-to-noise problems using contour deformations
Real-time lattice gauge theory actions: Unitarity, convergence, and path integral contour deformations
Journal Article
·
Mon May 24 00:00:00 EDT 2021
· Physical Review D
·
OSTI ID:2000987
+2 more
Mitigating Green's function Monte Carlo signal-to-noise problems using contour deformations
Journal Article
·
Thu Apr 06 00:00:00 EDT 2023
· TBD
·
OSTI ID:2000987
+1 more
Real-time lattice gauge theory actions: Unitarity, convergence, and path integral contour deformations
Journal Article
·
Fri Jul 30 00:00:00 EDT 2021
· Physical Review D
·
OSTI ID:2000987