Fermions and Supersymmetry in Neural Network Field Theories
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915629400850432 |
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| author | Frank, Samuel Halverson, James Maiti, Anindita Ruehle, Fabian |
| author_facet | Frank, Samuel Halverson, James Maiti, Anindita Ruehle, Fabian |
| contents | We introduce fermionic neural network field theories via Grassmann-valued neural networks. Free theories are obtained by a generalization of the Central Limit Theorem to Grassmann variables. This enables the realization of the free Dirac spinor at infinite width and a four fermion interaction at finite width. Yukawa couplings are introduced by breaking the statistical independence of the output weights for the fermionic and bosonic fields. A large class of interacting supersymmetric quantum mechanics and field theory models are introduced by super-affine transformations on the input that realize a superspace formalism. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16741 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fermions and Supersymmetry in Neural Network Field Theories Frank, Samuel Halverson, James Maiti, Anindita Ruehle, Fabian High Energy Physics - Theory Machine Learning We introduce fermionic neural network field theories via Grassmann-valued neural networks. Free theories are obtained by a generalization of the Central Limit Theorem to Grassmann variables. This enables the realization of the free Dirac spinor at infinite width and a four fermion interaction at finite width. Yukawa couplings are introduced by breaking the statistical independence of the output weights for the fermionic and bosonic fields. A large class of interacting supersymmetric quantum mechanics and field theory models are introduced by super-affine transformations on the input that realize a superspace formalism. |
| title | Fermions and Supersymmetry in Neural Network Field Theories |
| topic | High Energy Physics - Theory Machine Learning |
| url | https://arxiv.org/abs/2511.16741 |