Gauge covariant neural network for quarks and gluons
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| _version_ | 1866913801348055040 |
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| author | Nagai, Yuki Tomiya, Akio |
| author_facet | Nagai, Yuki Tomiya, Akio |
| contents | We propose gauge-covariant neural networks along with a specialized training algorithm for lattice QCD, designed to handle realistic quarks and gluons in four-dimensional space-time. We show that the smearing procedure can be interpreted as an extended version of residual neural networks with fixed parameters. To demonstrate the applicability of our neural networks, we develop a self-learning hybrid Monte Carlo algorithm in the context of two-color QCD, yielding outcomes consistent with those from the conventional Hybrid Monte Carlo approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2103_11965 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Gauge covariant neural network for quarks and gluons Nagai, Yuki Tomiya, Akio High Energy Physics - Lattice Disordered Systems and Neural Networks High Energy Physics - Theory We propose gauge-covariant neural networks along with a specialized training algorithm for lattice QCD, designed to handle realistic quarks and gluons in four-dimensional space-time. We show that the smearing procedure can be interpreted as an extended version of residual neural networks with fixed parameters. To demonstrate the applicability of our neural networks, we develop a self-learning hybrid Monte Carlo algorithm in the context of two-color QCD, yielding outcomes consistent with those from the conventional Hybrid Monte Carlo approach. |
| title | Gauge covariant neural network for quarks and gluons |
| topic | High Energy Physics - Lattice Disordered Systems and Neural Networks High Energy Physics - Theory |
| url | https://arxiv.org/abs/2103.11965 |