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 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.
- Authors:
- Publication Date:
- Research Org.:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1532580
- Alternate Identifier(s):
- OSTI ID: 1558963
- Report Number(s):
- LA-UR-18-26411
Journal ID: ISSN 2470-0010; PRVDAQ; 014504
- Grant/Contract Number:
- AC02-05CH11231; AC05-00OR22725; 89233218CNA000001
- Resource Type:
- Published Article
- Journal Name:
- Physical Review D
- Additional Journal Information:
- Journal Name: Physical Review D Journal Volume: 100 Journal Issue: 1; Journal ID: ISSN 2470-0010
- Publisher:
- American Physical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; Atomic, Nuclear and Particle Physics; Computer Science
Citation Formats
Yoon, Boram, Bhattacharya, Tanmoy, and Gupta, Rajan. Machine learning estimators for lattice QCD observables. United States: N. p., 2019.
Web. doi:10.1103/PhysRevD.100.014504.
Yoon, Boram, Bhattacharya, Tanmoy, & Gupta, Rajan. Machine learning estimators for lattice QCD observables. United States. https://doi.org/10.1103/PhysRevD.100.014504
Yoon, Boram, Bhattacharya, Tanmoy, and Gupta, Rajan. Tue .
"Machine learning estimators for lattice QCD observables". United States. https://doi.org/10.1103/PhysRevD.100.014504.
@article{osti_1532580,
title = {Machine learning estimators for lattice QCD observables},
author = {Yoon, Boram and Bhattacharya, Tanmoy and Gupta, Rajan},
abstractNote = {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.},
doi = {10.1103/PhysRevD.100.014504},
journal = {Physical Review D},
number = 1,
volume = 100,
place = {United States},
year = {2019},
month = {7}
}
https://doi.org/10.1103/PhysRevD.100.014504
Web of Science
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Works referencing / citing this record:
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