Machine Learning Gauged Supergravity
Chethan Krishnan, Vyshnav Mohan, Soham Ray
Abstract
Abstract Type IIB string theory on a 5‐sphere gives rise to gauged supergravity in five dimensions. Motivated by the fact that this is the context of the most widely studied example of the AdS/CFT correspondence, we undertake an investigation of its critical points. The scalar manifold is an coset, and the challenge is that it is 42‐dimensional. We take a Machine Learning approach to the problem using TensorFlow, and this results in a substantial increase in the number of known critical points. Our list of 32 critical points contains all five of the previously known ones, including an supersymmetric point identified by Khavaev, Pilch and Warner.
Topics & Concepts
Gauged supergravityContext (archaeology)String theorySupergravityPoint (geometry)Computer scienceString (physics)Artificial intelligenceTheoretical physicsScheme (mathematics)Scalar (mathematics)MathematicsScalar potentialManifold (fluid mechanics)Critical point (mathematics)Computational learning theoryPhysicsHigher-dimensional supergravityAlgorithmAlgebra over a fieldEmbeddingComplex manifoldTheoretical computer scienceBlack Holes and Theoretical PhysicsParticle physics theoretical and experimental studiesQuantum Chromodynamics and Particle Interactions