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Bibliographic Details
Main Author: Oh, Hyunwoo
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2501.14614
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author Oh, Hyunwoo
author_facet Oh, Hyunwoo
contents The precision of lattice QCD calculations is often hindered by the stochastic noise inherent in these methods. The control variates method can provide an effective noise reduction but are typically constructed using heuristic approaches, which may be inadequate for complex theories. In this work, we introduce a neural network-based framework for parametrizing control variates, eliminating the reliance on manual guesswork. Using $1+1$ dimensional scalar field theory as a test case, we demonstrate significant variance reduction, particularly in the strong coupling regime. Furthermore, we extend this approach to gauge theories, showcasing its potential to tackle signal-to-noise problems in diverse lattice QCD applications.
format Preprint
id arxiv_https___arxiv_org_abs_2501_14614
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Control variates with neural networks
Oh, Hyunwoo
High Energy Physics - Lattice
The precision of lattice QCD calculations is often hindered by the stochastic noise inherent in these methods. The control variates method can provide an effective noise reduction but are typically constructed using heuristic approaches, which may be inadequate for complex theories. In this work, we introduce a neural network-based framework for parametrizing control variates, eliminating the reliance on manual guesswork. Using $1+1$ dimensional scalar field theory as a test case, we demonstrate significant variance reduction, particularly in the strong coupling regime. Furthermore, we extend this approach to gauge theories, showcasing its potential to tackle signal-to-noise problems in diverse lattice QCD applications.
title Control variates with neural networks
topic High Energy Physics - Lattice
url https://arxiv.org/abs/2501.14614