Machine Learning the Conformal Manifold of Holographic CFT$_{2}$s

Bastien Duboeuf; Camille Eloy; Gabriel Larios

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arXiv · 2511.02981

Machine Learning the Conformal Manifold of Holographic CFT$_{2}$s

Bastien Duboeuf·Camille Eloy·Gabriel Larios

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Abstract

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.

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Bastien Duboeuf, Camille Eloy, Gabriel Larios. 2025-11-04. Machine Learning the Conformal Manifold of Holographic CFT$_{2}$s. https://arxiv.org/abs/2511.02981

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