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Machine learning action parameters in lattice quantum chromodynamics

Author(s)
Shanahan, Phiala E.; Trewartha, Daniel; Detmold, William
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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.
Date issued
2018-05
URI
http://hdl.handle.net/1721.1/115530
Department
Massachusetts Institute of Technology. Center for Theoretical Physics; Massachusetts Institute of Technology. Department of Physics
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
ISSN
2470-0010
2470-0029

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