Machine Learning the Conformal Manifold of Holographic CFT$_{2}$s
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912688223813632 |
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| author | Duboeuf, Bastien Eloy, Camille Larios, Gabriel |
| author_facet | Duboeuf, Bastien Eloy, Camille Larios, Gabriel |
| contents | We investigate the structure of conformal manifolds around AdS$_3 \times S^3$ which lift from continuous flat directions in the scalar potential of gauged supergravity resulting from six-dimensional $\mathcal{N}=(1,1)$ supergravity. Our approach combines numerical exploration and symbolic inference. For the latter, we develop a symbolic regression algorithm based on Annealed Sequential Monte Carlo samplers, a combination of Annealed Importance Sampling and Sequential Monte Carlo samplers, well-suited to uncovering polynomial constraints in high-dimensional parameter spaces. The algorithm reconstructs a set of polynomial relations that provides an explicit analytic parametrization of a new family of solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02981 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning the Conformal Manifold of Holographic CFT$_{2}$s Duboeuf, Bastien Eloy, Camille Larios, Gabriel High Energy Physics - Theory We investigate the structure of conformal manifolds around AdS$_3 \times S^3$ which lift from continuous flat directions in the scalar potential of gauged supergravity resulting from six-dimensional $\mathcal{N}=(1,1)$ supergravity. Our approach combines numerical exploration and symbolic inference. For the latter, we develop a symbolic regression algorithm based on Annealed Sequential Monte Carlo samplers, a combination of Annealed Importance Sampling and Sequential Monte Carlo samplers, well-suited to uncovering polynomial constraints in high-dimensional parameter spaces. The algorithm reconstructs a set of polynomial relations that provides an explicit analytic parametrization of a new family of solutions. |
| title | Machine Learning the Conformal Manifold of Holographic CFT$_{2}$s |
| topic | High Energy Physics - Theory |
| url | https://arxiv.org/abs/2511.02981 |