Multiscale Monte&nbsp;Carlo equilibration: Two-color QCD with two fermion flavors

Detmold, William; Endres, Michael G.

doi:10.1103/PhysRevD.94.114502

Multiscale Monte Carlo equilibration: Two-color QCD with two fermion flavors

Journal Article · Fri Dec 02 00:00:00 EST 2016 · Physical Review D

DOI:https://doi.org/10.1103/PhysRevD.94.114502· OSTI ID:1424917

Detmold, William ^[1]; Endres, Michael G. ^[2]

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Center for Theoretical Physics; Massachusetts Institute of Technology
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Center for Theoretical Physics

In this study, we demonstrate the applicability of a recently proposed multiscale thermalization algorithm to two-color quantum chromodynamics (QCD) with two mass-degenerate fermion flavors. The algorithm involves refining an ensemble of gauge configurations that had been generated using a renormalization group (RG) matched coarse action, thereby producing a fine ensemble that is close to the thermalized distribution of a target fine action; the refined ensemble is subsequently rethermalized using conventional algorithms. Although the generalization of this algorithm from pure Yang-Mills theory to QCD with dynamical fermions is straightforward, we find that in the latter case, the method is susceptible to numerical instabilities during the initial stages of rethermalization when using the hybrid Monte Carlo algorithm. We find that these instabilities arise from large fermion forces in the evolution, which are attributed to an accumulation of spurious near-zero modes of the Dirac operator. We propose a simple strategy for curing this problem, and demonstrate that rapid thermalization--as probed by a variety of gluonic and fermionic operators--is possible with the use of this solution. Also, we study the sensitivity of rethermalization rates to the RG matching of the coarse and fine actions, and identify effective matching conditions based on a variety of measured scales.

View Accepted Manuscript (DOE)

Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)

Grant/Contract Number:: SC0010495; SC0011090

OSTI ID:: 1424917

Alternate ID(s):: OSTI ID: 1334216

Journal Information:: Physical Review D, Journal Name: Physical Review D Journal Issue: 11 Vol. 94; ISSN PRVDAQ; ISSN 2470-0010

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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Machine learning action parameters in lattice quantum chromodynamics Shanahan, Phiala E.; Trewartha, Daniel; Detmold, William Physical Review D, Vol. 97, Issue 9 https://doi.org/10.1103/physrevd.97.094506	journal	May 2018
Flow-based generative models for Markov chain Monte Carlo in lattice field theory Albergo, M. S.; Kanwar, G.; Shanahan, P. E. arXiv https://doi.org/10.48550/arxiv.1904.12072	text	January 2019

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