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arXiv 1807.05971hep-lat
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格点量子色动力学观测量的机器学习估计

Machine Learning Estimators for Lattice QCD Observables

Boram Yoon, Tanmoy Bhattacharya, Rajan Gupta

备注：
8 pages, 5 figures
发表信息：
Phys. Rev. D 100, 014504 (2019)
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摘要：

提出了一种利用机器学习（ML）来降低计算格点量子色动力学（QCD）观测量的新方法。ML在背景规范场配置的子集（称为标记集）上进行训练，以根据在全样本上计算的相关但计算密集度较低的可观测量$\mathbf{X}$的值来预测可观测$O$。通过使用第二个子集（也是标记集的一部分），我们估计了训练ML算法预测结果的偏差。使用Boosted决策树回归ML算法，对于两种不同的格点量子色动力学计算，证明了计算成本减少约$7\%-38\%$：（1）预测由两点关联函数产生等矢量电荷的核子三点关联函数，（2）预测当小的电荷宇称（CP）破坏相互作用时，夸克的色电偶极矩相互作用，被添加到QCD，再次从两点关联函数计算没有CP破坏。

英文摘要：

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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