Machine learning the conformal manifold of holographic CFT2’s
Abstract
A bstract We investigate the structure of conformal manifolds around AdS 3 × S 3 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.
Fields of Study & Subjects
Black Holes and Theoretical Physics
Quantum Chromodynamics and Particle Interactions
Quantum many-body systems
Publication details
- Journal / source
- Journal of High Energy Physics
- Publisher
- Springer Science and Business Media LLC
- Volume
- 2026
- Issue
- 7
- Publication date
- 1 July 2026
- Language
- EN