10 papers
Learning Low Modes of the Wilson Dirac Operator with Gauge-Equivariant Networks
Minjae Choi, Hiroshi Ohno
In adaptive multigrid, test vectors that better approximate the low-mode subspace of the Wilson Dirac operator can reduce the solve time, but their construction can be costly.…
Bias-Corrected Machine-Learning Estimation of Chiral Condensate Cumulants: A Retrospective Lattice QCD Case Study
Benjamin J. Choi, Hiroshi Ohno, Akio Tomiya
We present a retrospective case study of bias-corrected machine learning (ML) estimates of traces of the inverse Dirac operator, (), using a fixed la…
A Machine Learning Approach for Lattice Gauge Fixing
Ho Hsiao, Benjamin J. Choi, Hiroshi Ohno +1
Gauge fixing is an essential step in lattice QCD calculations, particularly for studying gauge-dependent observables. Traditional iterative algorithms are computationally expensive…
Machine Learning-Based Estimation of Cumulants of Chiral Condensate via Multi-Ensemble Reweighting with Deborah.jl
Benjamin J. Choi, Hiroshi Ohno, Akio Tomiya
We investigate a bias-corrected machine learning (ML) strategy for estimating traces of the inverse Dirac operator, (), motivated by the need for hi…
Lattice Gauge Theory via LLVM-Level Automatic Differentiation
Yuki Nagai, Akio Tomiya, Hiroshi Ohno
We enable the automatic construction of Hybrid Monte Carlo (HMC) forces in lattice gauge theory by performing reverse-mode automatic differentiation at the level of optimized LLVM…
Sparse modeling study of extracting charmonium spectral functions from lattice QCD at finite temperature
Junichi Takahashi, Hiroshi Ohno, Akio Tomiya
We present charmonium spectral functions extracted from Euclidean-time correlation functions using sparse modeling (SpM). SpM solves inverse problems by considering only the sparsi…
CASK: A Gauge Covariant Transformer for Lattice Gauge Theory
Yuki Nagai, Hiroshi Ohno, Akio Tomiya
We propose a Transformer neural network architecture specifically designed for lattice QCD, focusing on preserving the fundamental symmetries required in lattice gauge theory. The…
Machine Learning Estimation on the Trace of Inverse Dirac Operator using the Gradient Boosting Decision Tree Regression
Benjamin J. Choi, Hiroshi Ohno, Takayuki Sumimoto +1
We present our preliminary results on the machine learning estimation of from other observables with the gradient boosting decision tree regression, where …
Sparse modeling study to extract spectral functions from lattice QCD data
Junichi Takahashi, Hiroshi Ohno, Akio Tomiya
We present spectral functions extracted from Euclidean-time correlation functions by using sparse modeling. Sparse modeling is a method that solves inverse problems by considering…
Sparse modeling approach to extract spectral functions with covariance of Euclidean-time correlators of lattice QCD
Junichi Takahashi, Hiroshi Ohno, Akio Tomiya
We present our sparse modeling study to extract spectral functions from Euclidean-time correlation functions. In this study covariance between different Euclidean times of the corr…