Machine learning estimators for lattice QCD observables
Abstract
A novel technique using machine learning (ML) to reduce the computational cost of evaluating lattice QCD observables is presented. The ML is trained on a subset of background gauge field configurations, called the labeled set, to predict an observable O from the values of correlated, but less computeintensive, observables X calculated on the full sample. By using a second subset, also part of the labeled set, we estimate the bias in the result predicted by the trained ML algorithm. A reduction in the computational cost by about 7%38% is demonstrated for two different lattice QCD calculations using the Boosted decision tree regression ML algorithm: (1) prediction of the nucleon threepoint correlation functions that yield isovector charges from the twopoint correlation functions and (2) prediction of the phase acquired by the neutron mass when a small C P violating interaction, the quark chromoelectric dipole moment interaction, is added to QCD, again from the twopoint correlation functions calculated without C P violation.
 Publication:

Physical Review D
 Pub Date:
 July 2019
 DOI:
 10.1103/PhysRevD.100.014504
 arXiv:
 arXiv:1807.05971
 Bibcode:
 2019PhRvD.100a4504Y
 Keywords:

 High Energy Physics  Lattice
 EPrint:
 8 pages, 5 figures