# 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}

}

DOI: 10.1103/PhysRevD.100.014504

*Citation information provided by*

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