Machine learning action parameters in lattice quantum chromodynamics
Name
PhysRevD.97.094506.pdf
Size
7.46 MB
Format
Adobe PDF
Checksum (MD5)
cba54d3108c5d801e2a5af117c59c8bd
Author(s)
Shanahan, Phiala E.
Trewartha, Daniel
Detmold, William
Date Issued
May 2018
Journal
Physical Review D
Publisher
American Physical Society
Citation
Shanahan, Phiala E. et al. "Machine learning action parameters in lattice quantum chromodynamics." Physical Reveiw D 97, 9 (May 2018): 094506
Version
Final published version
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 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.
MIT Department
Massachusetts Institute of Technology. Center for Theoretical Physics
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1103/PhysRevD.97.094506
