A new technique to incorporate multiple fermion flavors in tensor renormalization group method for lattice gauge theories
書誌事項
- 公開日
- 2023-11-27
- 資源種別
- journal article
- 権利情報
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- https://creativecommons.org/licenses/by/4.0
- https://creativecommons.org/licenses/by/4.0
- DOI
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- 10.1007/jhep11(2023)187
- 10.48550/arxiv.2309.01422
- 公開者
- Springer Science and Business Media LLC
説明
<jats:title>A<jats:sc>bstract</jats:sc> </jats:title><jats:p>We propose a new technique to incorporate multiple fermion flavors in the tensor renormalization group method for lattice gauge theories, where fermions are treated by the Grassmann tensor network formalism. The basic idea is to separate the site tensor into multiple layers associated with each flavor and to introduce the gauge field in each layer as replicas, which are all identified later. This formulation, after introducing an appropriate compression scheme in the network, enables us to reduce the size of the initial tensor with high efficiency compared with a naive implementation. The usefulness of this formulation is demonstrated by investigating the chiral phase transition and the Silver Blaze phenomenon in 2D Abelian gauge theories with <jats:italic>N</jats:italic><jats:sub>f</jats:sub> flavors of Wilson fermions up to <jats:italic>N</jats:italic><jats:sub>f</jats:sub> = 4.</jats:p>
収録刊行物
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- Journal of High Energy Physics
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Journal of High Energy Physics 2023 (11), 187-, 2023-11-27
Springer Science and Business Media LLC
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キーワード
- High Energy Physics - Theory
- Lattice Quantum Field Theory
- Statistical Mechanics (cond-mat.stat-mech)
- Field Theories in Lower Dimensions
- High Energy Physics - Lattice (hep-lat)
- FOS: Physical sciences
- QC770-798
- High Energy Physics - Lattice
- High Energy Physics - Theory (hep-th)
- Algorithms and Theoretical Developments
- Nuclear and particle physics. Atomic energy. Radioactivity
- Condensed Matter - Statistical Mechanics
- Finite Temperature or Finite Density
詳細情報 詳細情報について
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- CRID
- 1360021390739435392
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- ISSN
- 10298479
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- 資料種別
- journal article
-
- データソース種別
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- Crossref
- KAKEN
- OpenAIRE
