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Title: 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. doi:10.1103/PhysRevD.100.014504.
Yoon, Boram, Bhattacharya, Tanmoy, and Gupta, Rajan. Tue . "Machine learning estimators for lattice QCD observables". United States. doi: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}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1103/PhysRevD.100.014504

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Cited by: 3 works
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