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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.16329 |
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| _version_ | 1866911522159067136 |
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| author | Yasunaga, Shunsuke Yoshimura, Kenta Tomiya, Akio Nagai, Yuki |
| author_facet | Yasunaga, Shunsuke Yoshimura, Kenta Tomiya, Akio Nagai, Yuki |
| contents | 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 $L^3\times T\times L_5=4^3\times8\times8$ lattice demonstrate the feasibility of this framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16329 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Parameter Optimization of Domain-Wall Fermion using Machine Learning Yasunaga, Shunsuke Yoshimura, Kenta Tomiya, Akio Nagai, Yuki High Energy Physics - Lattice 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 $L^3\times T\times L_5=4^3\times8\times8$ lattice demonstrate the feasibility of this framework. |
| title | Parameter Optimization of Domain-Wall Fermion using Machine Learning |
| topic | High Energy Physics - Lattice |
| url | https://arxiv.org/abs/2603.16329 |