Learning non-Higgsable gauge groups in 4D F-theory

Understand

We apply machine learning techniques to solve a specific classification problem in 4D F-theory.

• For a divisor $D$ on a given complex threefold base, we want to read out the non-Higgsable gauge group on it using local geometric information near $D$.
• The input features are the triple intersection numbers among divisors near $D$ and the output label is the non-Higgsable gauge group.
• We use decision tree to solve this problem and achieved 85%-98% out-of-sample accuracies for different classes of divisors, where the data sets are generated from toric threefold bases without (4,6) curves.