Intelligent Explorations of the String Theory Landscape
This is an incremental review that aims to advance string phenomenology by applying AI to theoretical challenges in particle physics.
The paper reviews efforts to identify the Standard Model within string theory, focusing on the E8×E8 heterotic string compactified on Calabi-Yau threefolds, and discusses how machine learning can address mathematical hurdles in model building.
The goal of identifying the Standard Model of particle physics and its extensions within string theory has been one of the principal driving forces in string phenomenology. Recently, the incorporation of artificial intelligence in string theory and certain theoretical advancements have brought to light unexpected solutions to mathematical hurdles that have so far hindered progress in this direction. In this review we focus on model building efforts in the context of the $E_8\times E_8$ heterotic string compactified on smooth Calabi-Yau threefolds and discuss several areas in which machine learning is expected to make a difference.