10 papers
Dyon Condensation at from Wilson-'t Hooft Loops in Yang-Mills Theory
Hiromasa Watanabe, Issaku Kanamori, Okuto Morikawa +3
Although Yang--Mills theories at and are physically equivalent, their periodicity is realized nontrivially in the presence of a background field for the -form center…
Continuous Hasenbusch transport towards gauge diffusion with fermions
Akio Tomiya
Incorporating dynamical fermions is a central challenge for diffusion samplers of lattice gauge theories. We propose an analytic pseudofermion sampler based on continuous Hasenbusc…
Lattice Configuration Generation with a Self-Learning Diffusion Model
Akio Tomiya
We show that a diffusion sampler for lattice-field configurations can be self-trained without preparing target-ensemble training configurations using an external Monte Carlo calcul…
Numerical Hints for Dyon Condensation at via Wilson-'t Hooft Loops in Yang-Mills Theory
Hiromasa Watanabe, Issaku Kanamori, Okuto Morikawa +3
Yang-Mills theories at and are unitarily equivalent, but their periodicity has a nontrivial realization. Recent developments in generalized symmetries rigorously pr…
Thermodynamics in symmetry-improved Cornwall-Jackiw-Tomboulis formalism: application to the low-energy effective theory of QCD
Yuepeng Guan, Mamiya Kawaguchi, Shinya Matsuzaki +1
We study the thermodynamics of the symmetry-improved Cornwall-Jackiw-Tomboulis (SICJT) formalism and apply it to a low-energy effective theory of QCD. In the symmetry-improved form…
Parameter Optimization of Domain-Wall Fermion using Machine Learning
Shunsuke Yasunaga, Kenta Yoshimura, Akio Tomiya +1
We study a parameter optimization of domain-wall fermions to improve chiral symmetry based on machine learning. Domain-wall fermions involve coefficients along the fifth dimension,…
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…
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…
JuliaQCD: Portable lattice QCD package in Julia language
Yuki Nagai, Akio Tomiya
We develop a new lattice gauge theory code set JuliaQCD using the Julia language. Julia is well-suited for integrating machine learning techniques and enables rapid prototyping and…
Equivariant Transformer is all you need
Akio Tomiya, Yuki Nagai
Machine learning, deep learning, has been accelerating computational physics, which has been used to simulate systems on a lattice. Equivariance is essential to simulate a physical…