Machine learning action parameters in lattice quantum chromodynamics

Shanahan, Phiala; Trewartha, Daneil; Detmold, William

doi:10.1103/PhysRevD.97.094506

Title: Machine learning action parameters in lattice quantum chromodynamics

Journal Article · 16 May 2018 · Physical Review D

DOI: https://doi.org/10.1103/PhysRevD.97.094506 · OSTI ID:1437340

Shanahan, Phiala ^[1]; Trewartha, Daneil ^[2]; Detmold, William ^[3]

College of William and Mary, Williamsburg, VA (United States); Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Numerical lattice quantum chromodynamics studies of the strong interaction underpin theoretical understanding of many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The applicability of machine learning to this regression task is investigated, with deep neural networks found to provide an efficient solution even in cases where approaches such as principal component analysis fail. Finally, the high information content and complex symmetries inherent in lattice QCD datasets require custom neural network layers to be introduced and present opportunities for further development.

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Research Organization:: Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF)

Grant/Contract Number:: 0922770; SC0010495; SC0011090; SC0018121; AC05-06OR23177

OSTI ID:: 1437340

Alternate ID(s):: OSTI ID: 1438377; OSTI ID: 1635166

Report Number(s):: JLAB-THY-18-2627; DOE/OR/23177-4325; arXiv:1801.05784; MIT-CTP/4980; PRVDAQ; TRN: US1900429

Journal Information:: Physical Review D, Vol. 97, Issue 9; ISSN 2470-0010

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 39 works

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