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Title: Machine learning action parameters in lattice quantum chromodynamics

Abstract

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.

Authors:
; ;
Publication Date:
Research Org.:
Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF)
OSTI Identifier:
1437340
Alternate Identifier(s):
OSTI ID: 1438377
Report Number(s):
JLAB-THY-18-2627; DOE/OR/23177-4325; arXiv:1801.05784; MIT-CTP/4980
Journal ID: ISSN 2470-0010; PRVDAQ; 094506
Grant/Contract Number:  
SC0010495; SC0011090; SC0018121; AC05-06OR23177; 0922770
Resource Type:
Published Article
Journal Name:
Physical Review D
Additional Journal Information:
Journal Name: Physical Review D Journal Volume: 97 Journal Issue: 9; Journal ID: ISSN 2470-0010
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS

Citation Formats

Shanahan, Phiala E., Trewartha, Daniel, and Detmold, William. Machine learning action parameters in lattice quantum chromodynamics. United States: N. p., 2018. Web. doi:10.1103/PhysRevD.97.094506.
Shanahan, Phiala E., Trewartha, Daniel, & Detmold, William. Machine learning action parameters in lattice quantum chromodynamics. United States. doi:10.1103/PhysRevD.97.094506.
Shanahan, Phiala E., Trewartha, Daniel, and Detmold, William. Wed . "Machine learning action parameters in lattice quantum chromodynamics". United States. doi:10.1103/PhysRevD.97.094506.
@article{osti_1437340,
title = {Machine learning action parameters in lattice quantum chromodynamics},
author = {Shanahan, Phiala E. and Trewartha, Daniel and Detmold, William},
abstractNote = {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.},
doi = {10.1103/PhysRevD.97.094506},
journal = {Physical Review D},
number = 9,
volume = 97,
place = {United States},
year = {2018},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1103/PhysRevD.97.094506

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Cited by: 1 work
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