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<p>We investigate the structure of conformal manifolds around AdS<sub>3</sub> × <i>S</i><sup>3</sup> which lift from continuous flat directions in the scalar potential of gauged supergravity resulting from six-dimensional 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.</p>

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Machine learning the conformal manifold of holographic CFT2’s

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Bastien Duboeuf,
  • Camille Eloy,
  • Gabriel Larios

摘要

We investigate the structure of conformal manifolds around AdS3 × S3 which lift from continuous flat directions in the scalar potential of gauged supergravity resulting from six-dimensional 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.