Machine Learning Estimation on the Trace of Inverse Dirac Operator using the Gradient Boosting Decision Tree Regression

Fuente: arXiv
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Autores principales: Choi, Benjamin J., Ohno, Hiroshi, Sumimoto, Takayuki, Tomiya, Akio
Formato: Preprint
Publicado: 2024
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author Choi, Benjamin J.
Ohno, Hiroshi
Sumimoto, Takayuki
Tomiya, Akio
author_facet Choi, Benjamin J.
Ohno, Hiroshi
Sumimoto, Takayuki
Tomiya, Akio
contents We present our preliminary results on the machine learning estimation of $\text{Tr} \, M^{-n}$ from other observables with the gradient boosting decision tree regression, where $M$ is the Dirac operator. Ordinarily, $\text{Tr} \, M^{-n}$ is obtained by linear CG solver for stochastic sources which needs considerable computational cost. Hence, we explore the possibility of cost reduction on the trace estimation by the adoption of gradient boosting decision tree algorithm. We also discuss effects of bias and its correction.
format Preprint
id arxiv_https___arxiv_org_abs_2411_18170
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Machine Learning Estimation on the Trace of Inverse Dirac Operator using the Gradient Boosting Decision Tree Regression
Choi, Benjamin J.
Ohno, Hiroshi
Sumimoto, Takayuki
Tomiya, Akio
High Energy Physics - Lattice
We present our preliminary results on the machine learning estimation of $\text{Tr} \, M^{-n}$ from other observables with the gradient boosting decision tree regression, where $M$ is the Dirac operator. Ordinarily, $\text{Tr} \, M^{-n}$ is obtained by linear CG solver for stochastic sources which needs considerable computational cost. Hence, we explore the possibility of cost reduction on the trace estimation by the adoption of gradient boosting decision tree algorithm. We also discuss effects of bias and its correction.
title Machine Learning Estimation on the Trace of Inverse Dirac Operator using the Gradient Boosting Decision Tree Regression
topic High Energy Physics - Lattice
url https://arxiv.org/abs/2411.18170