Machine Learning String Standard Models
This work addresses the challenge of efficiently identifying viable string theory models for physicists, but it is incremental as it builds on existing methods in a specialized domain.
The paper tackled the problem of distinguishing consistent string compactification models with correct gauge groups and chiral asymmetry from random ones using machine learning, achieving this with relatively small neural networks and also through unsupervised learning with an auto-encoder, while learning non-topological properties like Higgs multiplet numbers required larger networks and enhanced data.
We study machine learning of phenomenologically relevant properties of string compactifications, which arise in the context of heterotic line bundle models. Both supervised and unsupervised learning are considered. We find that, for a fixed compactification manifold, relatively small neural networks are capable of distinguishing consistent line bundle models with the correct gauge group and the correct chiral asymmetry from random models without these properties. The same distinction can also be achieved in the context of unsupervised learning, using an auto-encoder. Learning non-topological properties, specifically the number of Higgs multiplets, turns out to be more difficult, but is possible using sizeable networks and feature-enhanced data sets.