Machine learning action parameters in lattice quantum chromodynamics - INSPIRE

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Machine learning action parameters in lattice quantum chromodynamics

Phiala E. Shanahan
(
- William-Mary Coll. and
- Jefferson Lab
)
,
Amalie Trewartha
(
- Jefferson Lab
)
,
William Detmold
(
- MIT, Cambridge, CTP
)

Jan 17, 2018

23 pages

Published in:

Phys.Rev.D 97 (2018) 9, 094506

Published: May 17, 2018

e-Print:

1801.05784 [hep-lat]

DOI:

10.1103/PhysRevD.97.094506

Report number:

MIT-CTP-4980,
JLAB-THY-18-2627,
MIT-CTP/4980

View in:

ADS Abstract Service

reference search64 citations

Citations per year

Abstract: (arXiv)

Numerical lattice quantum chromodynamics studies of the strong interaction are important in 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. 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.

11.15.Ha
12.38.Gc
lattice field theory
quantum chromodynamics
numerical calculations
gauge field theory
fermion: Wilson
neural network
lattice
principal component analysis

References(87)

Figures(35)

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