Parameter Optimization of Domain-Wall Fermion using Machine Learning
High Energy Physics - Lattice
2026-03-18 v1
Abstract
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, which can be treated as trainable parameters to reduce the chiral symmetry violation caused by the finite extent of the fifth dimension. As the loss function, we use the residual mass estimated stochastically on a single gauge configuration. Numerical tests on a lattice demonstrate the feasibility of this framework.
Cite
@article{arxiv.2603.16329,
title = {Parameter Optimization of Domain-Wall Fermion using Machine Learning},
author = {Shunsuke Yasunaga and Kenta Yoshimura and Akio Tomiya and Yuki Nagai},
journal= {arXiv preprint arXiv:2603.16329},
year = {2026}
}
Comments
8 pages, 3 figures, Contribution to the 42nd International Symposium on Lattice Field Theory (LATTICE2025)