Machine learning estimators for lattice QCD observables

Journal Article · 09 July 2019 · Physical Review D

DOI: https://doi.org/10.1103/PhysRevD.100.014504 · OSTI ID:1532580

Yoon, Boram  ^[1]; Bhattacharya, Tanmoy ^[1]; Gupta, Rajan ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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 compute-intensive, 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 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 CP violating interaction, the quark chromoelectric dipole moment interaction, is added to QCD, again from the two-point correlation functions calculated without CP violation.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: 89233218CNA000001; AC02-05CH11231; AC05-00OR22725
• OSTI ID: 1532580
• Alternate ID(s): OSTI ID: 1558963
• Report Number(s): LA-UR-18-26411; PRVDAQ; TRN: US2000263
• Journal Information: Physical Review D, Vol. 100, Issue 1; Conference: The 36th Annual International Symposium on Lattice Field Theory, 2018-07-22; ISSN 2470-0010
• Publisher: American Physical Society (APS)Copyright Statement
• Country of Publication: United States
• Language: English