Document Type

Article

Department/Program

Physics

Journal Title

PHYSICAL REVIEW D

Pub Date

5-16-2018

Publisher

American Physical Society

Volume

97

Issue

9

Abstract

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 arc 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.

DOI

https://doi.org/10.1103/PhysRevD.97.094506

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