Search arXiv⌕ Search

arXiv · hep-lat/9610016

Lattice Monte Carlo Data versus Perturbation Theory

Abstract

The differences between lattice Monte Carlo data and perturbation theory are usually associated with the ``bad'' behaviour of the bare lattice coupling g_0 due to the effects of large (and unknown) higher order coefficients in the g_0 perturbative series. In this philosophy a new, renormalised coupling g' is defined with the aim of reducing the higher order coefficients of the perturbative series in g'. An improvement in the agreement between lattice data and this new perturbation series is generally observed. In this paper an alternative scenario is discussed where lattice artifacts are proposed as the cause of the disagreement between lattice data and the g_0-perturbative series. We find that this interpretation provides excellent agreement between lattice data and perturbation theory in g_0 corrected for lattice artifacts. We show that this viewpoint leads typically to an order of magnitude improvement in the agreement between lattice data and perturbation theory, compared to typical g' perturbation expansions. The success of this procedure leads to a determination of Lambda_MSbar^{N_f=0} of 220 +- 20 MeV. Lattice data studied includes quenched values of the string tension, the hadronic scale r_0, the discrete beta function Deltaβ, M_rho, f_pi and the 1P-1S splitting in charmonium. The new 3-loop term of the lattice beta- function has been incorporated in this study. A discussion of the implication of this result for lattice calculations is presented.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

C. R. Allton. 1996-10-15. Lattice Monte Carlo Data versus Perturbation Theory. https://arxiv.org/abs/hep-lat/9610016

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Accurate Sampling from Diffusion Models

A new proposal called DM-SMC (Diffusion Model - Sequential Monte Carlo) is investigated, which samples ensembles defined in terms of an action, using diffusion models trained on samples from the ensemble. The SMC setup allows for accurate sampling in spite of an approximate diffusion model and the finite stepsize used in the numerical solution of the stochastic process. Improved update strategies are also investigated. Results are presented for a $Z_2$ symmetric scalar field theory in 2 dimensions near its 2nd order phase transition.

hep-lat↗

Decomposition of the axial-vector current in a finite box

We consider the matrix element of the axial-vector current between two nucleon states in a finite box. Starting from the chiral Lagrangian density with nucleon and Delta-isobar degrees of freedom, we study the finite-volume effects at the one-loop level. We show that the standard decomposition into the axial-vector and pseudoscalar form factor is incomplete in a finite box. We derive expressions for the complete set of in-box form factors at one loop, and demonstrate how to extract the full set from lattice correlation functions. We verify that the axial Ward identity holds in the chiral limit. We derive the one-loop expressions for the pseudoscalar form factor and verify that the in-box axial Ward identity away from the chiral limit is fulfilled also. Selected numerical results are shown for two flavor-SU(2) lattice ensembles. Sizable finite-volume effects are observed, with an important role for the Delta-isobar. We discuss the implications of our results for lattice studies of the axial-vector current. We conclude that full finite-box results are crucial for a precise determination of the form factors.

hep-lat↗

A variational framework for variance reduction in lattice field theory

The signal-to-noise problem limits the reach of many lattice calculations. We present a variational framework that recasts it as a transport problem: the loss of signal reflects a mismatch between the distribution one samples and the one needed to measure an observable, and can be reduced by transporting configurations to close that gap. The optimal transport is typically determined either through a stochastic estimator based on Langevin dynamics or by parametrising it as a normalising flow trained with automatic differentiation. We discuss how the framework brings these methods under a common variational principle and present results for scalar theories.

hep-lat↗