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arXiv · 1807.05971

Machine Learning Estimators for Lattice QCD Observables

Boram Yoon·Tanmoy Bhattacharya·Rajan Gupta
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Abstract

A novel technique using machine learning (ML) to reduce the computational cost of evaluating lattice quantum chromodynamics (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 compute-intensive, observables $\mathbf{X}$ 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 three-point correlation functions that yield isovector charges from the two-point correlation functions, and (2) prediction of the phase acquired by the neutron mass when a small Charge-Parity (CP) violating interaction, the quark chromoelectric dipole moment interaction, is added to QCD, again from the two-point correlation functions calculated without CP violation.

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Boram Yoon, Tanmoy Bhattacharya, Rajan Gupta. 2019-07-22. Machine Learning Estimators for Lattice QCD Observables. https://doi.org/10.1103/physrevd.100.014504

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