Paper
Parameter Optimization of Domain-Wall Fermion using Machine Learning
Authors
Shunsuke Yasunaga, Kenta Yoshimura, Akio Tomiya, Yuki Nagai
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 $L^3\times T\times L_5=4^3\times8\times8$ lattice demonstrate the feasibility of this framework.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.16329v1</id>\n <title>Parameter Optimization of Domain-Wall Fermion using Machine Learning</title>\n <updated>2026-03-17T10:01:10Z</updated>\n <link href='https://arxiv.org/abs/2603.16329v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.16329v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='hep-lat'/>\n <published>2026-03-17T10:01:10Z</published>\n <arxiv:comment>8 pages, 3 figures, Contribution to the 42nd International Symposium on Lattice Field Theory (LATTICE2025)</arxiv:comment>\n <arxiv:primary_category term='hep-lat'/>\n <author>\n <name>Shunsuke Yasunaga</name>\n </author>\n <author>\n <name>Kenta Yoshimura</name>\n </author>\n <author>\n <name>Akio Tomiya</name>\n </author>\n <author>\n <name>Yuki Nagai</name>\n </author>\n </entry>"
}