Machine Learning in High Energy Physics

 

Based on novel algorithms and techniques for machine learning and data science, we illustrate applications to string theory.
For instance, the Calabi-Yau manifolds enter the field of string theory through the compactification of the heterotic string. Topological quantities of manifolds, such as Betti or Hodge numbers, are often non-trivially related to the data describing the underlying manifold and tend to be difficult to work out. For this reason, such topological properties are an interesting and challenging playground for machine learning.