Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning

2022/03/16 by Steven Abel, Andrei Constantin, Thomas R. Harvey +2 · 4 citations
Physics and Astronomy · #Particle physics theoretical and experimental studies #Black Holes and Theoretical Physics #Quantum Chromodynamics and Particle Interactions

paper · pdf · doi:10.1002/prop.202200034

Abstract

Abstract The immensity of the string landscape and the difficulty of identifying solutions that match the observed features of particle physics have raised serious questions about the predictive power of string theory. Modern methods of optimisation and search can, however, significantly improve the prospects of constructing the standard model in string theory. In this paper we scrutinise a corner of the heterotic string landscape consisting of compactifications on Calabi‐Yau three‐folds with monad bundles and show that genetic algorithms can be successfully used to generate anomaly‐free supersymmetric GUTs with three families of fermions that have the right ingredients to accommodate the standard model. We compare this method with reinforcement learning and find that the two methods have similar efficacy but somewhat complementary characteristics.

Cited by

• Enumerating Calabi‐Yau Manifolds: Placing Bounds on the Number of Diffeomorphism Classes in the Kreuzer‐Skarke List
• Reinforcement Learning techniques for the flavor problem in particle physics
• Sampling String Vacua Using Generative Models
• Machine Learning the 6d Supergravity Landscape

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